Install packages needed.

We first create a vector of all the packages needed

packages <- c("agricolae", "dplyr", "plyr", "ggplot2", "readr", "ggpubr", "car",
              "rcompanion", "tidyverse", "ggsignif", "reshape")

Install packages not yet installed

installed_packages <- packages %in% rownames(installed.packages())
if (any(installed_packages == FALSE)) {
  install.packages(packages[!installed_packages])
}

Load all packages

invisible(lapply(packages, library, character.only = TRUE))
## 
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
## 
##     filter, lag
## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union
## ------------------------------------------------------------------------------
## You have loaded plyr after dplyr - this is likely to cause problems.
## If you need functions from both plyr and dplyr, please load plyr first, then dplyr:
## library(plyr); library(dplyr)
## ------------------------------------------------------------------------------
## 
## Attaching package: 'plyr'
## The following objects are masked from 'package:dplyr':
## 
##     arrange, count, desc, failwith, id, mutate, rename, summarise,
##     summarize
## 
## Attaching package: 'ggpubr'
## The following object is masked from 'package:plyr':
## 
##     mutate
## Loading required package: carData
## 
## Attaching package: 'car'
## The following object is masked from 'package:dplyr':
## 
##     recode
## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.1 ──
## ✓ tibble  3.1.4     ✓ stringr 1.4.0
## ✓ tidyr   1.1.3     ✓ forcats 0.5.1
## ✓ purrr   0.3.4
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## x plyr::arrange()   masks dplyr::arrange()
## x purrr::compact()  masks plyr::compact()
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## x plyr::failwith()  masks dplyr::failwith()
## x dplyr::filter()   masks stats::filter()
## x plyr::id()        masks dplyr::id()
## x dplyr::lag()      masks stats::lag()
## x ggpubr::mutate()  masks plyr::mutate(), dplyr::mutate()
## x car::recode()     masks dplyr::recode()
## x plyr::rename()    masks dplyr::rename()
## x purrr::some()     masks car::some()
## x plyr::summarise() masks dplyr::summarise()
## x plyr::summarize() masks dplyr::summarize()
## 
## Attaching package: 'reshape'
## The following objects are masked from 'package:tidyr':
## 
##     expand, smiths
## The following objects are masked from 'package:plyr':
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##     rename, round_any
## The following object is masked from 'package:dplyr':
## 
##     rename

Set the working directory to the directory where the output files will be saved.

In this case, we assume you have cloned/donwloaded this repository to your “Documents” folder.

Change directory on mac/linux:

setwd(“/Users/YOURUSERNAME/Documents/X.necrophora.secondaryMetabolites/output”)

Change directory on Windows (Windows 10 in this example):

setwd(“C:/Users/YOURUSERNAME/Documents/X.necrophora.secondaryMetabolites/output”)

For this demosntration, we did not export the files in PDF to the output directory. If you wish to do so, do the following:

Step 1: Call the pdf command to start the plot

pdf(file = "/Users/YOURUSERNAME/Documents/X.necrophora.secondaryMetabolites/output/ Figure1.pdf",   # The directory you want to save the file in

width = 7, # The width of the plot in inches

height = 5) # The height of the plot in inches

Step 2: Add the code provided below for your desired plot.

Step 3: Run dev.off() to create the file!

dev.off()

For this example, we set the working directory to the following:

setwd("/Users/tedggarcia/Documents/X.necrophora.secondaryMetabolites/output/")

Loading digital chlorophyll content datasets (only one repetition of each experiment for illustration purposes). All datasets can be found in the folder named “raw_data”

ES2 = First experiment for 14 Days of exporuse (DOE)

#ES4 = Repetetion for 14 DOE

ES5 = First experiment for 7 DOE

#ES8 = Repetition for 7 DOE

#ES13A = Experiment testing potentially resistant cultivars (7DOE)

ES13B = Repetition of ES13A

ES14A = Experiment testing effects among plant species (7DOE)

#ES14B = Repetition of ES14A

ES2 <- read.csv("../raw_data/ES2.ChlorophyllContent.14DOE.Exp1.csv", header = T)
ES5 <- read.csv("../raw_data/ES5.ChlorophyllContent.7DOE.Exp1.csv", header = T)
ES13B <- read.csv("../raw_data/ES13B.ChlorophyllContent.7DOE.Exp2.Cultivars.csv",
                  header = T)
ES14A <- read.csv("../raw_data/ES14A.ChlorophyllContent.7DOE.Exp1.PlantSpecies.csv", 
                  header = T)

Run Shapiro-Wilk Tests to check for normality

shapiro.test(ES2$chl)
## 
##  Shapiro-Wilk normality test
## 
## data:  ES2$chl
## W = 0.74674, p-value < 2.2e-16
shapiro.test(ES5$chl)
## 
##  Shapiro-Wilk normality test
## 
## data:  ES5$chl
## W = 0.95514, p-value = 5.341e-10
shapiro.test(ES13B$chl)
## 
##  Shapiro-Wilk normality test
## 
## data:  ES13B$chl
## W = 0.95496, p-value = 2.7e-07
shapiro.test(ES14A$chl)
## 
##  Shapiro-Wilk normality test
## 
## data:  ES14A$chl
## W = 0.95203, p-value = 1.513e-06

Check the distribution of the data and assess if normalization is needed.

ggdensity(ES2$chl, main = "Density of Chlorophyll Content (digital) for ES2",
          xlab = "Datapoints")
## Warning: Removed 60 rows containing non-finite values (stat_density).

ggdensity(ES5$chl, main = "Density of Chlorophyll Content (digital) for ES5", 
          xlab = "Datapoints")
## Warning: Removed 12 rows containing non-finite values (stat_density).

ggdensity(ES13B$chl, main = "Density of Chlorophyll Content (digital) for ES13B", 
          xlab = "Datapoints")
## Warning: Removed 6 rows containing non-finite values (stat_density).

ggdensity(ES14A$chl, main = "Density of Chlorophyll Content (digital) for ES14A", 
          xlab = "Datapoints")
## Warning: Removed 3 rows containing non-finite values (stat_density).

ggqqplot(ES2$chl)
## Warning: Removed 60 rows containing non-finite values (stat_qq).
## Warning: Removed 60 rows containing non-finite values (stat_qq_line).

## Warning: Removed 60 rows containing non-finite values (stat_qq_line).

ggqqplot(ES5$chl)
## Warning: Removed 12 rows containing non-finite values (stat_qq).
## Warning: Removed 12 rows containing non-finite values (stat_qq_line).

## Warning: Removed 12 rows containing non-finite values (stat_qq_line).

ggqqplot(ES13B$chl)
## Warning: Removed 6 rows containing non-finite values (stat_qq).
## Warning: Removed 6 rows containing non-finite values (stat_qq_line).

## Warning: Removed 6 rows containing non-finite values (stat_qq_line).

ggqqplot(ES14A$chl)
## Warning: Removed 3 rows containing non-finite values (stat_qq).
## Warning: Removed 3 rows containing non-finite values (stat_qq_line).

## Warning: Removed 3 rows containing non-finite values (stat_qq_line).

plotNormalHistogram(ES2$chl, main = "Density of Chlorophyll Content (Digital) for ES2", 
                    xlab = "Datapoints")

plotNormalHistogram(ES5$chl, main = "Density of Chlorophyll Content (Digital) for ES5", 
                    xlab = "Datapoints")

plotNormalHistogram(ES13B$chl, main = "Density of Chlorophyll Content (Digital) for E13B",
                    xlab = "Datapoints")

plotNormalHistogram(ES14A$chl, main = "Density of Chlorophyll Content (Digital) for E14A",
                    xlab = "Datapoints")

Use the Tukey’s tranformation method to normalize the distribution and append to datasets

ES2_chl.tuk = transformTukey(ES2$chl, plotit=FALSE)
## 
##     lambda      W Shapiro.p.value
## 416  0.375 0.9449       3.664e-09
## 
## if (lambda >  0){TRANS = x ^ lambda} 
## if (lambda == 0){TRANS = log(x)} 
## if (lambda <  0){TRANS = -1 * x ^ lambda}
ES5_chl.tuk = transformTukey(ES5$chl, plotit=FALSE)
## 
##     lambda      W Shapiro.p.value
## 427   0.65 0.9695       1.098e-07
## 
## if (lambda >  0){TRANS = x ^ lambda} 
## if (lambda == 0){TRANS = log(x)} 
## if (lambda <  0){TRANS = -1 * x ^ lambda}
ES13B_chl.tuk = transformTukey(ES13B$chl, plotit=FALSE)
## 
##     lambda      W Shapiro.p.value
## 432  0.775 0.9604       1.226e-06
## 
## if (lambda >  0){TRANS = x ^ lambda} 
## if (lambda == 0){TRANS = log(x)} 
## if (lambda <  0){TRANS = -1 * x ^ lambda}
ES14A_chl.tuk = transformTukey(ES14A$chl, plotit=FALSE)
## 
##     lambda     W Shapiro.p.value
## 470  1.725 0.979         0.00282
## 
## if (lambda >  0){TRANS = x ^ lambda} 
## if (lambda == 0){TRANS = log(x)} 
## if (lambda <  0){TRANS = -1 * x ^ lambda}

Append the transformed values to original datasets

ES2.mod <- cbind(ES2, ES2_chl.tuk)
ES5.mod <- cbind(ES5, ES5_chl.tuk)
ES13B.mod <- cbind(ES13B, ES13B_chl.tuk)
ES14A.mod <- cbind(ES14A, ES14A_chl.tuk)

Statistical analyses

Run ANOVA and Tukey’s honest significance differences for raw chlorophyll content.

ES2 dataset (untransformed data)

As desribed above, this experiment was ran using cell-free culture filtrates (CFCFs) from three local strains of Xylaria necrophora (DMCC2126, DMCC2127, and DMCC2165) and one Colletotrichum siamense (DMCC2966) for 14 days (ES2)

###############ES2 analysis (raw data)################################
ES2.chl.anova <- lm (ES2$chl ~ ES2$Treatment + 
                       ES2$Dilution + 
                       ES2$Condition + 
                       ES2$isoRep + 
                       ES2$techRep + 
                       ES2$sampleNumber)
ES2.chl.anova
## 
## Call:
## lm(formula = ES2$chl ~ ES2$Treatment + ES2$Dilution + ES2$Condition + 
##     ES2$isoRep + ES2$techRep + ES2$sampleNumber)
## 
## Coefficients:
##             (Intercept)    ES2$TreatmentDMCC2126    ES2$TreatmentDMCC2127  
##                 236.806                 -140.175                 -173.159  
##   ES2$TreatmentDMCC2165    ES2$TreatmentDMCC2966       ES2$Dilution25fold  
##                -169.865                  -44.126                 -102.848  
## ES2$ConditionStationary    ES2$isoRepisolateRep2         ES2$techRepStem2  
##                  -8.823                   23.729                  -24.695  
##        ES2$techRepStem3  ES2$sampleNumbersample2  ES2$sampleNumbersample3  
##                  16.950                   26.386                   30.435
summary(ES2.chl.anova)
## 
## Call:
## lm(formula = ES2$chl ~ ES2$Treatment + ES2$Dilution + ES2$Condition + 
##     ES2$isoRep + ES2$techRep + ES2$sampleNumber)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -231.24  -49.47    1.55   41.40  536.42 
## 
## Coefficients:
##                         Estimate Std. Error t value Pr(>|t|)    
## (Intercept)              236.806     20.365  11.628  < 2e-16 ***
## ES2$TreatmentDMCC2126   -140.175     18.956  -7.395 1.70e-12 ***
## ES2$TreatmentDMCC2127   -173.159     19.204  -9.017  < 2e-16 ***
## ES2$TreatmentDMCC2165   -169.865     18.952  -8.963  < 2e-16 ***
## ES2$TreatmentDMCC2966    -44.126     18.481  -2.388   0.0176 *  
## ES2$Dilution25fold      -102.848     11.998  -8.572 7.35e-16 ***
## ES2$ConditionStationary   -8.823     11.944  -0.739   0.4607    
## ES2$isoRepisolateRep2     23.729     11.964   1.983   0.0483 *  
## ES2$techRepStem2         -24.695     15.316  -1.612   0.1080    
## ES2$techRepStem3          16.950     14.020   1.209   0.2277    
## ES2$sampleNumbersample2   26.386     14.436   1.828   0.0687 .  
## ES2$sampleNumbersample3   30.435     14.489   2.101   0.0366 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 100.5 on 276 degrees of freedom
##   (72 observations deleted due to missingness)
## Multiple R-squared:  0.4591, Adjusted R-squared:  0.4375 
## F-statistic:  21.3 on 11 and 276 DF,  p-value: < 2.2e-16
anova(ES2.chl.anova)
## Analysis of Variance Table
## 
## Response: ES2$chl
##                   Df  Sum Sq Mean Sq F value    Pr(>F)    
## ES2$Treatment      4 1458908  364727 36.1018 < 2.2e-16 ***
## ES2$Dilution       1  732380  732380 72.4932 1.094e-15 ***
## ES2$Condition      1    3246    3246  0.3213   0.57128    
## ES2$isoRep         1   38119   38119  3.7732   0.05310 .  
## ES2$techRep        2   80731   40366  3.9955   0.01947 *  
## ES2$sampleNumber   2   53280   26640  2.6369   0.07338 .  
## Residuals        276 2788355   10103                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#Tukey's HSD for Variable chl by Treament
ES2.chl.treatment.HSD.test <- HSD.test(ES2.chl.anova, 'ES2$Treatment', group = T)
ES2.chl.treatment.HSD.test
## $statistics
##    MSerror  Df     Mean       CV
##   10102.73 276 105.3393 95.41771
## 
## $parameters
##    test        name.t ntr StudentizedRange alpha
##   Tukey ES2$Treatment   5         3.883285  0.05
## 
## $means
##            ES2$chl       std  r Min     Max       Q25     Q50       Q75
## control  206.91423 217.07353 57   0 831.472  26.54900 138.046 272.67000
## DMCC2126  73.25279  74.61783 57   0 281.899  11.31300  29.554 129.60000
## DMCC2127  37.91085  49.89550 54   0 167.994   8.52575  15.327  49.05425
## DMCC2165  30.48823  45.19861 57   0 187.945   8.36200  14.000  20.43000
## DMCC2966 167.98710  89.73008 63   0 309.266 119.20850 177.714 233.30650
## 
## $comparison
## NULL
## 
## $groups
##            ES2$chl groups
## control  206.91423      a
## DMCC2966 167.98710      a
## DMCC2126  73.25279      b
## DMCC2127  37.91085      b
## DMCC2165  30.48823      b
## 
## attr(,"class")
## [1] "group"
#Tukey's HSD for Variable chl by Dilution
ES2.chl.dilution.HSD.test <- HSD.test(ES2.chl.anova, 'ES2$Dilution', group = T)
ES2.chl.dilution.HSD.test
## $statistics
##    MSerror  Df     Mean       CV
##   10102.73 276 105.3393 95.41771
## 
## $parameters
##    test       name.t ntr StudentizedRange alpha
##   Tukey ES2$Dilution   2         2.784016  0.05
## 
## $means
##           ES2$chl       std   r Min     Max      Q25      Q50       Q75
## 100fold 157.13270 159.97363 138   0 831.472 36.10000 129.1440 206.71875
## 25fold   57.68939  79.35162 150   0 309.266  9.85425  15.6685  99.75575
## 
## $comparison
## NULL
## 
## $groups
##           ES2$chl groups
## 100fold 157.13270      a
## 25fold   57.68939      b
## 
## attr(,"class")
## [1] "group"
#Complete ANOVA for ES2 by treatment by dilution
ES2.comp.HSD.group <- HSD.test(ES2.chl.anova, c("ES2$Treatment", "ES2$Dilution"), group=TRUE,console=TRUE)
## 
## Study: ES2.chl.anova ~ c("ES2$Treatment", "ES2$Dilution")
## 
## HSD Test for ES2$chl 
## 
## Mean Square Error:  10102.73 
## 
## ES2$Treatment:ES2$Dilution,  means
## 
##                     ES2.chl        std  r    Min     Max
## control:100fold  383.864000 223.675014 24 97.748 831.472
## control:25fold    78.223485  77.070835 33  0.000 268.776
## DMCC2126:100fold 127.480933  64.977439 30 10.433 281.899
## DMCC2126:25fold   12.999296  10.944223 27  0.000  51.676
## DMCC2127:100fold  58.980593  59.597226 27  0.000 167.994
## DMCC2127:25fold   16.841111  24.515869 27  0.000 112.319
## DMCC2165:100fold  58.801375  58.889805 24  0.000 187.945
## DMCC2165:25fold    9.896848   6.632284 33  0.000  19.414
## DMCC2966:100fold 171.013333  97.165275 33  0.000 301.867
## DMCC2966:25fold  164.658233  82.303611 30  0.000 309.266
## 
## Alpha: 0.05 ; DF Error: 276 
## Critical Value of Studentized Range: 4.511094 
## 
## Groups according to probability of means differences and alpha level( 0.05 )
## 
## Treatments with the same letter are not significantly different.
## 
##                     ES2$chl groups
## control:100fold  383.864000      a
## DMCC2966:100fold 171.013333      b
## DMCC2966:25fold  164.658233      b
## DMCC2126:100fold 127.480933     bc
## control:25fold    78.223485     cd
## DMCC2127:100fold  58.980593     cd
## DMCC2165:100fold  58.801375     cd
## DMCC2127:25fold   16.841111      d
## DMCC2126:25fold   12.999296      d
## DMCC2165:25fold    9.896848      d
ES2.comp.HSD.group
## $statistics
##    MSerror  Df     Mean       CV
##   10102.73 276 105.3393 95.41771
## 
## $parameters
##    test                     name.t ntr StudentizedRange alpha
##   Tukey ES2$Treatment:ES2$Dilution  10         4.511094  0.05
## 
## $means
##                     ES2$chl        std  r    Min     Max       Q25      Q50
## control:100fold  383.864000 223.675014 24 97.748 831.472 244.69000 280.5385
## control:25fold    78.223485  77.070835 33  0.000 268.776  15.68300  59.4900
## DMCC2126:100fold 127.480933  64.977439 30 10.433 281.899  81.64425 129.1440
## DMCC2126:25fold   12.999296  10.944223 27  0.000  51.676   9.86550  11.3130
## DMCC2127:100fold  58.980593  59.597226 27  0.000 167.994  12.11000  35.6240
## DMCC2127:25fold   16.841111  24.515869 27  0.000 112.319   0.00000  11.9040
## DMCC2165:100fold  58.801375  58.889805 24  0.000 187.945  14.21225  25.3885
## DMCC2165:25fold    9.896848   6.632284 33  0.000  19.414   0.00000  12.2830
## DMCC2966:100fold 171.013333  97.165275 33  0.000 301.867 118.40500 176.8540
## DMCC2966:25fold  164.658233  82.303611 30  0.000 309.266 120.78250 181.5795
##                       Q75
## control:100fold  527.0058
## control:25fold   129.7670
## DMCC2126:100fold 159.8775
## DMCC2126:25fold   16.5335
## DMCC2127:100fold  90.5650
## DMCC2127:25fold   15.6860
## DMCC2165:100fold 105.9032
## DMCC2165:25fold   14.7740
## DMCC2966:100fold 241.9460
## DMCC2966:25fold  222.5877
## 
## $comparison
## NULL
## 
## $groups
##                     ES2$chl groups
## control:100fold  383.864000      a
## DMCC2966:100fold 171.013333      b
## DMCC2966:25fold  164.658233      b
## DMCC2126:100fold 127.480933     bc
## control:25fold    78.223485     cd
## DMCC2127:100fold  58.980593     cd
## DMCC2165:100fold  58.801375     cd
## DMCC2127:25fold   16.841111      d
## DMCC2126:25fold   12.999296      d
## DMCC2165:25fold    9.896848      d
## 
## attr(,"class")
## [1] "group"

Same analysis using the normalized dataset

###############ES2 analysis (normalized dataset) ################################
ES2.mod.chl.anova <- lm (ES2.mod$ES2_chl.tuk ~ ES2.mod$Treatment + 
                           ES2.mod$Dilution + 
                           ES2.mod$Condition + 
                           ES2.mod$isoRep + 
                           ES2.mod$techRep + 
                           ES2.mod$sampleNumber)
ES2.mod.chl.anova
## 
## Call:
## lm(formula = ES2.mod$ES2_chl.tuk ~ ES2.mod$Treatment + ES2.mod$Dilution + 
##     ES2.mod$Condition + ES2.mod$isoRep + ES2.mod$techRep + ES2.mod$sampleNumber)
## 
## Coefficients:
##                 (Intercept)    ES2.mod$TreatmentDMCC2126  
##                     7.52662                     -2.19660  
##   ES2.mod$TreatmentDMCC2127    ES2.mod$TreatmentDMCC2165  
##                    -3.39025                     -3.45003  
##   ES2.mod$TreatmentDMCC2966       ES2.mod$Dilution25fold  
##                    -0.21011                     -2.34945  
## ES2.mod$ConditionStationary    ES2.mod$isoRepisolateRep2  
##                    -0.09975                      0.73788  
##        ES2.mod$techRepStem2         ES2.mod$techRepStem3  
##                    -0.70265                     -0.27113  
## ES2.mod$sampleNumbersample2  ES2.mod$sampleNumbersample3  
##                    -0.03389                     -0.09430
summary(ES2.mod.chl.anova)
## 
## Call:
## lm(formula = ES2.mod$ES2_chl.tuk ~ ES2.mod$Treatment + ES2.mod$Dilution + 
##     ES2.mod$Condition + ES2.mod$isoRep + ES2.mod$techRep + ES2.mod$sampleNumber)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -7.1829 -1.1889  0.4416  1.2936  4.5838 
## 
## Coefficients:
##                             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                  7.52662    0.44329  16.979  < 2e-16 ***
## ES2.mod$TreatmentDMCC2126   -2.19660    0.41262  -5.323 2.11e-07 ***
## ES2.mod$TreatmentDMCC2127   -3.39025    0.41803  -8.110 1.67e-14 ***
## ES2.mod$TreatmentDMCC2165   -3.45003    0.41254  -8.363 3.06e-15 ***
## ES2.mod$TreatmentDMCC2966   -0.21011    0.40229  -0.522  0.60190    
## ES2.mod$Dilution25fold      -2.34945    0.26117  -8.996  < 2e-16 ***
## ES2.mod$ConditionStationary -0.09975    0.26000  -0.384  0.70152    
## ES2.mod$isoRepisolateRep2    0.73788    0.26043   2.833  0.00495 ** 
## ES2.mod$techRepStem2        -0.70265    0.33340  -2.108  0.03597 *  
## ES2.mod$techRepStem3        -0.27113    0.30518  -0.888  0.37510    
## ES2.mod$sampleNumbersample2 -0.03389    0.31425  -0.108  0.91420    
## ES2.mod$sampleNumbersample3 -0.09430    0.31539  -0.299  0.76518    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.188 on 276 degrees of freedom
##   (72 observations deleted due to missingness)
## Multiple R-squared:  0.4559, Adjusted R-squared:  0.4342 
## F-statistic: 21.02 on 11 and 276 DF,  p-value: < 2.2e-16
anova(ES2.mod.chl.anova)
## Analysis of Variance Table
## 
## Response: ES2.mod$ES2_chl.tuk
##                       Df  Sum Sq Mean Sq F value    Pr(>F)    
## ES2.mod$Treatment      4  680.08  170.02 35.5165 < 2.2e-16 ***
## ES2.mod$Dilution       1  367.55  367.55 76.7802 < 2.2e-16 ***
## ES2.mod$Condition      1    0.63    0.63  0.1326  0.716072    
## ES2.mod$isoRep         1   36.95   36.95  7.7190  0.005839 ** 
## ES2.mod$techRep        2   21.22   10.61  2.2166  0.110912    
## ES2.mod$sampleNumber   2    0.44    0.22  0.0456  0.955457    
## Residuals            276 1321.23    4.79                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#Tukey's HSD for Variable chl (tukey trans) by Treament
ES2.mod.chl.treatment.HSD.test <- HSD.test(ES2.mod.chl.anova, 'ES2.mod$Treatment', group = T)
ES2.mod.chl.treatment.HSD.test
## $statistics
##    MSerror  Df     Mean       CV
##   4.787063 276 4.479861 48.83937
## 
## $parameters
##    test            name.t ntr StudentizedRange alpha
##   Tukey ES2.mod$Treatment   5         3.883285  0.05
## 
## $means
##          ES2.mod$ES2_chl.tuk      std  r Min       Max      Q25      Q50
## control             6.207956 3.276161 57   0 12.443509 3.419937 6.346130
## DMCC2126            4.140619 2.307227 57   0  8.294402 2.483657 3.560255
## DMCC2127            2.929858 2.131941 54   0  6.831014 2.232076 2.783162
## DMCC2165            2.663168 1.976045 57   0  7.124617 2.217514 2.690283
## DMCC2966            6.195529 2.505798 63   0  8.587655 6.006381 6.976629
##               Q75
## control  8.191511
## DMCC2126 6.197648
## DMCC2127 4.305207
## DMCC2165 3.099921
## DMCC2966 7.725989
## 
## $comparison
## NULL
## 
## $groups
##          ES2.mod$ES2_chl.tuk groups
## control             6.207956      a
## DMCC2966            6.195529      a
## DMCC2126            4.140619      b
## DMCC2127            2.929858      c
## DMCC2165            2.663168      c
## 
## attr(,"class")
## [1] "group"
#Tukey's HSD for Variable chl (tukey trans) by Dilution
ES2.mod.chl.dilution.HSD.test <- HSD.test(ES2.mod.chl.anova, 'ES2.mod$Dilution', group = T)
ES2.mod.chl.dilution.HSD.test
## $statistics
##    MSerror  Df     Mean       CV
##   4.787063 276 4.479861 48.83937
## 
## $parameters
##    test           name.t ntr StudentizedRange alpha
##   Tukey ES2.mod$Dilution   2         2.784016  0.05
## 
## $means
##         ES2.mod$ES2_chl.tuk      std   r Min       Max      Q25      Q50
## 100fold            5.670079 2.877306 138   0 12.443509 3.837417 6.189452
## 25fold             3.384861 2.482893 150   0  8.587655 2.358352 2.806307
##              Q75
## 100fold 7.383524
## 25fold  5.616963
## 
## $comparison
## NULL
## 
## $groups
##         ES2.mod$ES2_chl.tuk groups
## 100fold            5.670079      a
## 25fold             3.384861      b
## 
## attr(,"class")
## [1] "group"
#Complete ANOVA for ES2.mod by treatment by dilution (tukey trans)
ES2.mod.comp.HSD.group <- HSD.test(ES2.mod.chl.anova, c("ES2.mod$Treatment", 
                                                        "ES2.mod$Dilution"), 
                                                        group=TRUE,console=TRUE)
## 
## Study: ES2.mod.chl.anova ~ c("ES2.mod$Treatment", "ES2.mod$Dilution")
## 
## HSD Test for ES2.mod$ES2_chl.tuk 
## 
## Mean Square Error:  4.787063 
## 
## ES2.mod$Treatment:ES2.mod$Dilution,  means
## 
##                  ES2.mod.ES2_chl.tuk      std  r      Min       Max
## control:100fold             8.952842 2.033695 24 5.575585 12.443509
## control:25fold              4.211675 2.459674 33 0.000000  8.147445
## DMCC2126:100fold            5.904452 1.432971 30 2.409370  8.294402
## DMCC2126:25fold             2.180805 1.263683 27 0.000000  4.390190
## DMCC2127:100fold            3.720246 2.309541 27 0.000000  6.831014
## DMCC2127:25fold             2.139470 1.622868 27 0.000000  5.873811
## DMCC2165:100fold            3.677465 2.368645 24 0.000000  7.124617
## DMCC2165:25fold             1.925497 1.211620 33 0.000000  3.041187
## DMCC2966:100fold            6.114039 2.778697 33 0.000000  8.510026
## DMCC2966:25fold             6.285168 2.210961 30 0.000000  8.587655
## 
## Alpha: 0.05 ; DF Error: 276 
## Critical Value of Studentized Range: 4.511094 
## 
## Groups according to probability of means differences and alpha level( 0.05 )
## 
## Treatments with the same letter are not significantly different.
## 
##                  ES2.mod$ES2_chl.tuk groups
## control:100fold             8.952842      a
## DMCC2966:25fold             6.285168      b
## DMCC2966:100fold            6.114039      b
## DMCC2126:100fold            5.904452     bc
## control:25fold              4.211675     cd
## DMCC2127:100fold            3.720246     de
## DMCC2165:100fold            3.677465     de
## DMCC2126:25fold             2.180805      e
## DMCC2127:25fold             2.139470      e
## DMCC2165:25fold             1.925497      e
ES2.mod.comp.HSD.group
## $statistics
##    MSerror  Df     Mean       CV
##   4.787063 276 4.479861 48.83937
## 
## $parameters
##    test                             name.t ntr StudentizedRange alpha
##   Tukey ES2.mod$Treatment:ES2.mod$Dilution  10         4.511094  0.05
## 
## $means
##                  ES2.mod$ES2_chl.tuk      std  r      Min       Max      Q25
## control:100fold             8.952842 2.033695 24 5.575585 12.443509 7.860042
## control:25fold              4.211675 2.459674 33 0.000000  8.147445 2.807281
## DMCC2126:100fold            5.904452 1.432971 30 2.409370  8.294402 5.211560
## DMCC2126:25fold             2.180805 1.263683 27 0.000000  4.390190 2.359361
## DMCC2127:100fold            3.720246 2.309541 27 0.000000  6.831014 2.547399
## DMCC2127:25fold             2.139470 1.622868 27 0.000000  5.873811 0.000000
## DMCC2165:100fold            3.677465 2.368645 24 0.000000  7.124617 2.700544
## DMCC2165:25fold             1.925497 1.211620 33 0.000000  3.041187 0.000000
## DMCC2966:100fold            6.114039 2.778697 33 0.000000  8.510026 5.991199
## DMCC2966:25fold             6.285168 2.210961 30 0.000000  8.587655 6.035946
##                       Q50       Q75
## control:100fold  8.279323 10.486003
## control:25fold   4.628247  6.200641
## DMCC2126:100fold 6.189452  6.705312
## DMCC2126:25fold  2.483657  2.863395
## DMCC2127:100fold 3.818594  5.417472
## DMCC2127:25fold  2.531540  2.807481
## DMCC2165:100fold 3.362478  5.745663
## DMCC2165:25fold  2.561469  2.745123
## DMCC2966:100fold 6.963949  7.832392
## DMCC2966:25fold  7.032779  7.590879
## 
## $comparison
## NULL
## 
## $groups
##                  ES2.mod$ES2_chl.tuk groups
## control:100fold             8.952842      a
## DMCC2966:25fold             6.285168      b
## DMCC2966:100fold            6.114039      b
## DMCC2126:100fold            5.904452     bc
## control:25fold              4.211675     cd
## DMCC2127:100fold            3.720246     de
## DMCC2165:100fold            3.677465     de
## DMCC2126:25fold             2.180805      e
## DMCC2127:25fold             2.139470      e
## DMCC2165:25fold             1.925497      e
## 
## attr(,"class")
## [1] "group"

Run analyses for ES5

This test was run for 7 DOE and photos were taken of the last day of exposure.

###############ES5 analysis################################
ES5.chl.anova <- lm (ES5$chl ~ ES5$Treatment + 
                               ES5$Dilution + 
                               ES5$Condition + 
                               ES5$isoRep + 
                               ES5$techRep + 
                               ES5$sampleNumber)
ES5.chl.anova
## 
## Call:
## lm(formula = ES5$chl ~ ES5$Treatment + ES5$Dilution + ES5$Condition + 
##     ES5$isoRep + ES5$techRep + ES5$sampleNumber)
## 
## Coefficients:
##             (Intercept)    ES5$TreatmentDMCC2126    ES5$TreatmentDMCC2127  
##                 192.365                  -61.618                  -70.990  
##   ES5$TreatmentDMCC2165       ES5$Dilution25fold  ES5$ConditionStationary  
##                 -67.429                  -46.539                   42.178  
##   ES5$isoRepisolateRep2    ES5$isoRepisolateRep3      ES5$techRepstemRep2  
##                  -9.981                  -22.792                  -14.269  
##     ES5$techRepstemRep3  ES5$sampleNumbersample2  ES5$sampleNumbersample3  
##                  19.985                   11.399                   25.312
summary(ES5.chl.anova)
## 
## Call:
## lm(formula = ES5$chl ~ ES5$Treatment + ES5$Dilution + ES5$Condition + 
##     ES5$isoRep + ES5$techRep + ES5$sampleNumber)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -182.445  -40.817   -5.474   42.676  187.396 
## 
## Coefficients:
##                         Estimate Std. Error t value Pr(>|t|)    
## (Intercept)              192.365     10.836  17.753  < 2e-16 ***
## ES5$TreatmentDMCC2126    -61.618      8.801  -7.001 1.05e-11 ***
## ES5$TreatmentDMCC2127    -70.990      8.734  -8.128 5.27e-15 ***
## ES5$TreatmentDMCC2165    -67.429      8.798  -7.664 1.33e-13 ***
## ES5$Dilution25fold       -46.539      6.177  -7.534 3.19e-13 ***
## ES5$ConditionStationary   42.178      6.177   6.828 3.13e-11 ***
## ES5$isoRepisolateRep2     -9.981      7.580  -1.317 0.188662    
## ES5$isoRepisolateRep3    -22.792      7.534  -3.025 0.002642 ** 
## ES5$techRepstemRep2      -14.269      7.620  -1.873 0.061849 .  
## ES5$techRepstemRep3       19.985      7.536   2.652 0.008315 ** 
## ES5$sampleNumbersample2   11.399      7.557   1.509 0.132197    
## ES5$sampleNumbersample3   25.312      7.557   3.350 0.000884 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 63.22 on 408 degrees of freedom
##   (12 observations deleted due to missingness)
## Multiple R-squared:  0.3665, Adjusted R-squared:  0.3494 
## F-statistic: 21.46 on 11 and 408 DF,  p-value: < 2.2e-16
anova(ES5.chl.anova)
## Analysis of Variance Table
## 
## Response: ES5$chl
##                   Df  Sum Sq Mean Sq F value    Pr(>F)    
## ES5$Treatment      3  351053  117018 29.2750 < 2.2e-16 ***
## ES5$Dilution       1  239796  239796 59.9912 7.615e-14 ***
## ES5$Condition      1  186231  186231 46.5904 3.179e-11 ***
## ES5$isoRep         2   37850   18925  4.7345  0.009275 ** 
## ES5$techRep        2   83616   41808 10.4593 3.717e-05 ***
## ES5$sampleNumber   2   44997   22498  5.6285  0.003879 ** 
## Residuals        408 1630853    3997                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#Tukey's HSD for Variable chl by Treament
ES5.chl.treatment.HSD.test <- HSD.test(ES5.chl.anova, 'ES5$Treatment', group = T)
ES5.chl.treatment.HSD.test
## $statistics
##    MSerror  Df     Mean       CV
##   3997.188 408 143.1371 44.16975
## 
## $parameters
##    test        name.t ntr StudentizedRange alpha
##   Tukey ES5$Treatment   4         3.648176  0.05
## 
## $means
##           ES5$chl      std   r  Min   Max     Q25   Q50    Q75
## control  193.8353 69.20948 102 26.0 372.6 147.750 202.9 240.55
## DMCC2126 131.8714 73.80466 105 30.3 277.2  63.700 110.8 189.60
## DMCC2127 122.4120 75.41655 108  0.0 339.2  64.875 100.3 157.95
## DMCC2165 126.4705 73.67261 105  0.0 289.2  68.300 100.0 189.20
## 
## $comparison
## NULL
## 
## $groups
##           ES5$chl groups
## control  193.8353      a
## DMCC2126 131.8714      b
## DMCC2165 126.4705      b
## DMCC2127 122.4120      b
## 
## attr(,"class")
## [1] "group"
#Tukey's HSD for Variable chl by Dilution
ES5.chl.dilution.HSD.test <- HSD.test(ES5.chl.anova, 'ES5$Dilution', group = T)
ES5.chl.dilution.HSD.test
## $statistics
##    MSerror  Df     Mean       CV      MSD
##   3997.188 408 143.1371 44.16975 12.12889
## 
## $parameters
##    test       name.t ntr StudentizedRange alpha
##   Tukey ES5$Dilution   2         2.780054  0.05
## 
## $means
##          ES5$chl      std   r Min   Max    Q25    Q50     Q75
## 100fold 166.9881 77.60533 210   0 372.6 99.475 178.20 232.825
## 25fold  119.2862 71.77681 210   0 303.7 61.000  94.45 174.500
## 
## $comparison
## NULL
## 
## $groups
##          ES5$chl groups
## 100fold 166.9881      a
## 25fold  119.2862      b
## 
## attr(,"class")
## [1] "group"
#Tukey's HSD for Variable chl by Condition
ES5.chl.cond.HSD.test <- HSD.test(ES5.chl.anova, 'ES5$Condition', group = T)
ES5.chl.cond.HSD.test
## $statistics
##    MSerror  Df     Mean       CV      MSD
##   3997.188 408 143.1371 44.16975 12.12889
## 
## $parameters
##    test        name.t ntr StudentizedRange alpha
##   Tukey ES5$Condition   2         2.780054  0.05
## 
## $means
##             ES5$chl      std   r Min   Max    Q25    Q50     Q75
## Shaking    121.3619 70.21004 210   0 363.9 63.900  99.15 174.075
## Stationary 164.9124 80.22074 210   0 372.6 91.075 179.40 234.325
## 
## $comparison
## NULL
## 
## $groups
##             ES5$chl groups
## Stationary 164.9124      a
## Shaking    121.3619      b
## 
## attr(,"class")
## [1] "group"
#Complete ANOVA for ES5 by treatment by condition, by dilution
ES5.comp.HSD.group <- HSD.test(ES5.chl.anova, c("ES5$Treatment", "ES5$Condition", 
                                                "ES5$Dilution"), group=TRUE,console=TRUE)
## 
## Study: ES5.chl.anova ~ c("ES5$Treatment", "ES5$Condition", "ES5$Dilution")
## 
## HSD Test for ES5$chl 
## 
## Mean Square Error:  3997.188 
## 
## ES5$Treatment:ES5$Condition:ES5$Dilution,  means
## 
##                               ES5.chl      std  r   Min   Max
## control:Shaking:100fold     200.02083 68.81458 24 104.0 363.9
## control:Shaking:25fold      158.22593 62.18883 27  26.0 249.0
## control:Stationary:100fold  238.50000 37.84527 27 185.5 372.6
## control:Stationary:25fold   177.46250 78.47053 24  37.7 303.7
## DMCC2126:Shaking:100fold    161.77500 70.00547 24  48.0 270.4
## DMCC2126:Shaking:25fold      75.53333 30.56325 27  30.3 140.8
## DMCC2126:Stationary:100fold 174.24815 63.63720 27  51.7 264.4
## DMCC2126:Stationary:25fold  119.25185 79.48387 27  36.0 277.2
## DMCC2127:Shaking:100fold     93.23333 39.13111 27  37.1 190.5
## DMCC2127:Shaking:25fold      61.84444 32.99067 27   0.0 119.5
## DMCC2127:Stationary:100fold 192.10370 77.79170 27  75.0 339.2
## DMCC2127:Stationary:25fold  142.46667 67.68053 27  53.1 296.0
## DMCC2165:Shaking:100fold    143.79630 71.66806 27  36.8 273.2
## DMCC2165:Shaking:25fold      89.69630 40.84195 27  40.3 174.5
## DMCC2165:Stationary:100fold 135.31852 85.93666 27   0.0 279.9
## DMCC2165:Stationary:25fold  138.39583 79.51052 24  48.0 289.2
## 
## Alpha: 0.05 ; DF Error: 408 
## Critical Value of Studentized Range: 4.87582 
## 
## Groups according to probability of means differences and alpha level( 0.05 )
## 
## Treatments with the same letter are not significantly different.
## 
##                               ES5$chl groups
## control:Stationary:100fold  238.50000      a
## control:Shaking:100fold     200.02083     ab
## DMCC2127:Stationary:100fold 192.10370    abc
## control:Stationary:25fold   177.46250   abcd
## DMCC2126:Stationary:100fold 174.24815    bcd
## DMCC2126:Shaking:100fold    161.77500    bcd
## control:Shaking:25fold      158.22593    bcd
## DMCC2165:Shaking:100fold    143.79630   bcde
## DMCC2127:Stationary:25fold  142.46667   bcde
## DMCC2165:Stationary:25fold  138.39583   bcde
## DMCC2165:Stationary:100fold 135.31852    cde
## DMCC2126:Stationary:25fold  119.25185    def
## DMCC2127:Shaking:100fold     93.23333     ef
## DMCC2165:Shaking:25fold      89.69630     ef
## DMCC2126:Shaking:25fold      75.53333      f
## DMCC2127:Shaking:25fold      61.84444      f
ES5.comp.HSD.group
## $statistics
##    MSerror  Df     Mean       CV
##   3997.188 408 143.1371 44.16975
## 
## $parameters
##    test                                   name.t ntr StudentizedRange alpha
##   Tukey ES5$Treatment:ES5$Condition:ES5$Dilution  16          4.87582  0.05
## 
## $means
##                               ES5$chl      std  r   Min   Max     Q25    Q50
## control:Shaking:100fold     200.02083 68.81458 24 104.0 363.9 146.800 186.90
## control:Shaking:25fold      158.22593 62.18883 27  26.0 249.0 126.400 174.80
## control:Stationary:100fold  238.50000 37.84527 27 185.5 372.6 215.850 235.10
## control:Stationary:25fold   177.46250 78.47053 24  37.7 303.7 124.350 193.05
## DMCC2126:Shaking:100fold    161.77500 70.00547 24  48.0 270.4  87.125 173.00
## DMCC2126:Shaking:25fold      75.53333 30.56325 27  30.3 140.8  50.050  66.50
## DMCC2126:Stationary:100fold 174.24815 63.63720 27  51.7 264.4 132.600 180.00
## DMCC2126:Stationary:25fold  119.25185 79.48387 27  36.0 277.2  56.400  85.00
## DMCC2127:Shaking:100fold     93.23333 39.13111 27  37.1 190.5  69.800  85.80
## DMCC2127:Shaking:25fold      61.84444 32.99067 27   0.0 119.5  45.400  58.90
## DMCC2127:Stationary:100fold 192.10370 77.79170 27  75.0 339.2 109.700 204.20
## DMCC2127:Stationary:25fold  142.46667 67.68053 27  53.1 296.0  78.050 131.20
## DMCC2165:Shaking:100fold    143.79630 71.66806 27  36.8 273.2  78.650 113.60
## DMCC2165:Shaking:25fold      89.69630 40.84195 27  40.3 174.5  60.100  77.90
## DMCC2165:Stationary:100fold 135.31852 85.93666 27   0.0 279.9  61.950 158.00
## DMCC2165:Stationary:25fold  138.39583 79.51052 24  48.0 289.2  73.150 114.70
##                                 Q75
## control:Shaking:100fold     245.325
## control:Shaking:25fold      205.700
## control:Stationary:100fold  253.200
## control:Stationary:25fold   238.750
## DMCC2126:Shaking:100fold    207.875
## DMCC2126:Shaking:25fold      94.400
## DMCC2126:Stationary:100fold 230.800
## DMCC2126:Stationary:25fold  173.250
## DMCC2127:Shaking:100fold    116.550
## DMCC2127:Shaking:25fold      88.250
## DMCC2127:Stationary:100fold 249.300
## DMCC2127:Stationary:25fold  186.700
## DMCC2165:Shaking:100fold    201.750
## DMCC2165:Shaking:25fold      94.600
## DMCC2165:Stationary:100fold 205.300
## DMCC2165:Stationary:25fold  191.025
## 
## $comparison
## NULL
## 
## $groups
##                               ES5$chl groups
## control:Stationary:100fold  238.50000      a
## control:Shaking:100fold     200.02083     ab
## DMCC2127:Stationary:100fold 192.10370    abc
## control:Stationary:25fold   177.46250   abcd
## DMCC2126:Stationary:100fold 174.24815    bcd
## DMCC2126:Shaking:100fold    161.77500    bcd
## control:Shaking:25fold      158.22593    bcd
## DMCC2165:Shaking:100fold    143.79630   bcde
## DMCC2127:Stationary:25fold  142.46667   bcde
## DMCC2165:Stationary:25fold  138.39583   bcde
## DMCC2165:Stationary:100fold 135.31852    cde
## DMCC2126:Stationary:25fold  119.25185    def
## DMCC2127:Shaking:100fold     93.23333     ef
## DMCC2165:Shaking:25fold      89.69630     ef
## DMCC2126:Shaking:25fold      75.53333      f
## DMCC2127:Shaking:25fold      61.84444      f
## 
## attr(,"class")
## [1] "group"

Same analyses for ES5, using normalized data

###############ES5 analysis (normalized data) ################################
ES5.mod.chl.anova <- lm (ES5.mod$ES5_chl.tuk ~ ES5.mod$Treatment +
                                                ES5.mod$Dilution + 
                                                ES5.mod$Condition + 
                                                ES5.mod$isoRep + 
                                                ES5.mod$techRep + 
                                                ES5.mod$sampleNumber)
ES5.mod.chl.anova
## 
## Call:
## lm(formula = ES5.mod$ES5_chl.tuk ~ ES5.mod$Treatment + ES5.mod$Dilution + 
##     ES5.mod$Condition + ES5.mod$isoRep + ES5.mod$techRep + ES5.mod$sampleNumber)
## 
## Coefficients:
##                 (Intercept)    ES5.mod$TreatmentDMCC2126  
##                      30.278                       -7.067  
##   ES5.mod$TreatmentDMCC2127    ES5.mod$TreatmentDMCC2165  
##                      -8.357                       -7.928  
##      ES5.mod$Dilution25fold  ES5.mod$ConditionStationary  
##                      -5.443                        4.789  
##   ES5.mod$isoRepisolateRep2    ES5.mod$isoRepisolateRep3  
##                      -1.403                       -2.930  
##     ES5.mod$techRepstemRep2      ES5.mod$techRepstemRep3  
##                      -1.392                        2.517  
## ES5.mod$sampleNumbersample2  ES5.mod$sampleNumbersample3  
##                       1.050                        2.548
summary(ES5.mod.chl.anova)
## 
## Call:
## lm(formula = ES5.mod$ES5_chl.tuk ~ ES5.mod$Treatment + ES5.mod$Dilution + 
##     ES5.mod$Condition + ES5.mod$isoRep + ES5.mod$techRep + ES5.mod$sampleNumber)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -28.2842  -4.6883  -0.0798   5.3904  19.2000 
## 
## Coefficients:
##                             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                  30.2777     1.3012  23.270  < 2e-16 ***
## ES5.mod$TreatmentDMCC2126    -7.0672     1.0568  -6.687 7.50e-11 ***
## ES5.mod$TreatmentDMCC2127    -8.3567     1.0488  -7.968 1.63e-14 ***
## ES5.mod$TreatmentDMCC2165    -7.9283     1.0565  -7.505 3.90e-13 ***
## ES5.mod$Dilution25fold       -5.4428     0.7417  -7.338 1.18e-12 ***
## ES5.mod$ConditionStationary   4.7890     0.7417   6.457 3.05e-10 ***
## ES5.mod$isoRepisolateRep2    -1.4026     0.9102  -1.541  0.12411    
## ES5.mod$isoRepisolateRep3    -2.9300     0.9047  -3.239  0.00130 ** 
## ES5.mod$techRepstemRep2      -1.3920     0.9150  -1.521  0.12896    
## ES5.mod$techRepstemRep3       2.5171     0.9049   2.782  0.00566 ** 
## ES5.mod$sampleNumbersample2   1.0500     0.9074   1.157  0.24789    
## ES5.mod$sampleNumbersample3   2.5484     0.9074   2.808  0.00522 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 7.592 on 408 degrees of freedom
##   (12 observations deleted due to missingness)
## Multiple R-squared:  0.3506, Adjusted R-squared:  0.3331 
## F-statistic: 20.02 on 11 and 408 DF,  p-value: < 2.2e-16
anova(ES5.mod.chl.anova)
## Analysis of Variance Table
## 
## Response: ES5.mod$ES5_chl.tuk
##                       Df  Sum Sq Mean Sq F value    Pr(>F)    
## ES5.mod$Treatment      3  4830.6  1610.2 27.9375 < 2.2e-16 ***
## ES5.mod$Dilution       1  3271.4  3271.4 56.7598 3.204e-13 ***
## ES5.mod$Condition      1  2403.9  2403.9 41.7082 3.018e-10 ***
## ES5.mod$isoRep         2   618.1   309.1  5.3623  0.005027 ** 
## ES5.mod$techRep        2  1110.4   555.2  9.6327 8.172e-05 ***
## ES5.mod$sampleNumber   2   459.3   229.6  3.9845  0.019330 *  
## Residuals            408 23515.2    57.6                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#Tukey's HSD for Variable chl (tukey trans) by Treament
ES5.mod.chl.treatment.HSD.test <- HSD.test(ES5.mod.chl.anova, 'ES5.mod$Treatment', 
                                           group = T)
ES5.mod.chl.treatment.HSD.test
## $statistics
##    MSerror  Df     Mean       CV
##   57.63529 408 24.21363 31.35338
## 
## $parameters
##    test            name.t ntr StudentizedRange alpha
##   Tukey ES5.mod$Treatment   4         3.648176  0.05
## 
## $means
##          ES5.mod$ES5_chl.tuk      std   r      Min      Max      Q25      Q50
## control             30.14562 7.609997 102 8.312519 46.91458 25.71521 31.60326
## DMCC2126            23.01374 8.709822 105 9.182009 38.70932 14.88301 21.32803
## DMCC2127            21.72436 9.135639 108 0.000000 44.13634 15.06086 19.99151
## DMCC2165            22.21139 9.151154 105 0.000000 39.79045 15.57304 19.95262
##               Q75
## control  35.30039
## DMCC2126 30.24091
## DMCC2127 26.85501
## DMCC2165 30.19943
## 
## $comparison
## NULL
## 
## $groups
##          ES5.mod$ES5_chl.tuk groups
## control             30.14562      a
## DMCC2126            23.01374      b
## DMCC2165            22.21139      b
## DMCC2127            21.72436      b
## 
## attr(,"class")
## [1] "group"
#Tukey's HSD for Variable chl (tukey trans) by Dilution
ES5.mod.chl.dilution.HSD.test <- HSD.test(ES5.mod.chl.anova, 'ES5.mod$Dilution', 
                                          group = T)
ES5.mod.chl.dilution.HSD.test
## $statistics
##    MSerror  Df     Mean       CV      MSD
##   57.63529 408 24.21363 31.35338 1.456424
## 
## $parameters
##    test           name.t ntr StudentizedRange alpha
##   Tukey ES5.mod$Dilution   2         2.780054  0.05
## 
## $means
##         ES5.mod$ES5_chl.tuk      std   r Min      Max      Q25      Q50
## 100fold            26.99820 9.029696 210   0 46.91458 19.88445 29.04621
## 25fold             21.42906 8.725273 210   0 41.07609 14.46985 19.22561
##              Q75
## 100fold 34.55964
## 25fold  28.65280
## 
## $comparison
## NULL
## 
## $groups
##         ES5.mod$ES5_chl.tuk groups
## 100fold            26.99820      a
## 25fold             21.42906      b
## 
## attr(,"class")
## [1] "group"
#Tukey's HSD for Variable chl (tukey trans) by Condition
ES5.mod.chl.cond.HSD.test <- HSD.test(ES5.mod.chl.anova, 'ES5.mod$Condition', group = T)
ES5.mod.chl.cond.HSD.test
## $statistics
##    MSerror  Df     Mean       CV      MSD
##   57.63529 408 24.21363 31.35338 1.456424
## 
## $parameters
##    test            name.t ntr StudentizedRange alpha
##   Tukey ES5.mod$Condition   2         2.780054  0.05
## 
## $means
##            ES5.mod$ES5_chl.tuk      std   r Min      Max      Q25      Q50
## Shaking               21.73611 8.509070 210   0 46.19961 14.91331 19.84222
## Stationary            26.69114 9.407897 210   0 46.91458 18.77627 29.17320
##                 Q75
## Shaking    28.60736
## Stationary 34.70420
## 
## $comparison
## NULL
## 
## $groups
##            ES5.mod$ES5_chl.tuk groups
## Stationary            26.69114      a
## Shaking               21.73611      b
## 
## attr(,"class")
## [1] "group"
#Complete ANOVA for ES5.mod by treatment by condition, by dilution (tukey trans)
ES5.mod.comp.HSD.group <- HSD.test(ES5.mod.chl.anova, c("ES5.mod$Treatment",
                                                        "ES5.mod$Condition",
                                                        "ES5.mod$Dilution"),
                                                        group=TRUE,console=TRUE)
## 
## Study: ES5.mod.chl.anova ~ c("ES5.mod$Treatment", "ES5.mod$Condition", "ES5.mod$Dilution")
## 
## HSD Test for ES5.mod$ES5_chl.tuk 
## 
## Mean Square Error:  57.63529 
## 
## ES5.mod$Treatment:ES5.mod$Condition:ES5.mod$Dilution,  means
## 
##                             ES5.mod.ES5_chl.tuk       std  r       Min      Max
## control:Shaking:100fold                30.92160  6.877550 24 20.467824 46.19961
## control:Shaking:25fold                 26.28822  7.584437 27  8.312519 36.10186
## control:Stationary:100fold             35.01510  3.511991 27 29.814226 46.91458
## control:Stationary:25fold              28.23105  8.847762 24 10.583319 41.07609
## DMCC2126:Shaking:100fold               26.63321  8.061394 24 12.382456 38.08942
## DMCC2126:Shaking:25fold                16.33068  4.334703 27  9.182009 24.92251
## DMCC2126:Stationary:100fold            28.13045  7.218510 27 12.994778 37.53790
## DMCC2126:Stationary:25fold             21.36280  9.304178 27 10.270619 38.70932
## DMCC2127:Shaking:100fold               18.70574  5.119409 27 10.473529 30.33414
## DMCC2127:Shaking:25fold                13.80400  6.101857 27  0.000000 22.40212
## DMCC2127:Stationary:100fold            29.90084  8.268740 27 16.549688 44.13634
## DMCC2127:Stationary:25fold             24.48688  7.726440 27 13.222435 40.39612
## DMCC2165:Shaking:100fold               24.56023  8.205647 27 10.418401 38.34533
## DMCC2165:Shaking:25fold                18.20998  5.258465 27 11.052188 28.65280
## DMCC2165:Stationary:100fold            22.51380 11.836979 27  0.000000 38.95398
## DMCC2165:Stationary:25fold             23.73032  9.225861 24 12.382456 39.79045
## 
## Alpha: 0.05 ; DF Error: 408 
## Critical Value of Studentized Range: 4.87582 
## 
## Groups according to probability of means differences and alpha level( 0.05 )
## 
## Treatments with the same letter are not significantly different.
## 
##                             ES5.mod$ES5_chl.tuk groups
## control:Stationary:100fold             35.01510      a
## control:Shaking:100fold                30.92160     ab
## DMCC2127:Stationary:100fold            29.90084     ab
## control:Stationary:25fold              28.23105    abc
## DMCC2126:Stationary:100fold            28.13045    abc
## DMCC2126:Shaking:100fold               26.63321     bc
## control:Shaking:25fold                 26.28822     bc
## DMCC2165:Shaking:100fold               24.56023    bcd
## DMCC2127:Stationary:25fold             24.48688    bcd
## DMCC2165:Stationary:25fold             23.73032    bcd
## DMCC2165:Stationary:100fold            22.51380    cde
## DMCC2126:Stationary:25fold             21.36280    cde
## DMCC2127:Shaking:100fold               18.70574    def
## DMCC2165:Shaking:25fold                18.20998    def
## DMCC2126:Shaking:25fold                16.33068     ef
## DMCC2127:Shaking:25fold                13.80400      f
ES5.mod.comp.HSD.group
## $statistics
##    MSerror  Df     Mean       CV
##   57.63529 408 24.21363 31.35338
## 
## $parameters
##    test                                               name.t ntr
##   Tukey ES5.mod$Treatment:ES5.mod$Condition:ES5.mod$Dilution  16
##   StudentizedRange alpha
##            4.87582  0.05
## 
## $means
##                             ES5.mod$ES5_chl.tuk       std  r       Min      Max
## control:Shaking:100fold                30.92160  6.877550 24 20.467824 46.19961
## control:Shaking:25fold                 26.28822  7.584437 27  8.312519 36.10186
## control:Stationary:100fold             35.01510  3.511991 27 29.814226 46.91458
## control:Stationary:25fold              28.23105  8.847762 24 10.583319 41.07609
## DMCC2126:Shaking:100fold               26.63321  8.061394 24 12.382456 38.08942
## DMCC2126:Shaking:25fold                16.33068  4.334703 27  9.182009 24.92251
## DMCC2126:Stationary:100fold            28.13045  7.218510 27 12.994778 37.53790
## DMCC2126:Stationary:25fold             21.36280  9.304178 27 10.270619 38.70932
## DMCC2127:Shaking:100fold               18.70574  5.119409 27 10.473529 30.33414
## DMCC2127:Shaking:25fold                13.80400  6.101857 27  0.000000 22.40212
## DMCC2127:Stationary:100fold            29.90084  8.268740 27 16.549688 44.13634
## DMCC2127:Stationary:25fold             24.48688  7.726440 27 13.222435 40.39612
## DMCC2165:Shaking:100fold               24.56023  8.205647 27 10.418401 38.34533
## DMCC2165:Shaking:25fold                18.20998  5.258465 27 11.052188 28.65280
## DMCC2165:Stationary:100fold            22.51380 11.836979 27  0.000000 38.95398
## DMCC2165:Stationary:25fold             23.73032  9.225861 24 12.382456 39.79045
##                                  Q25      Q50      Q75
## control:Shaking:100fold     25.60774 29.95578 35.75395
## control:Shaking:25fold      23.23414 28.68481 31.88435
## control:Stationary:100fold  32.89985 34.77877 36.49626
## control:Stationary:25fold   22.97996 30.57655 35.12646
## DMCC2126:Shaking:100fold    18.24222 28.49017 32.10254
## DMCC2126:Shaking:25fold     12.72170 15.30503 19.21892
## DMCC2126:Stationary:100fold 23.96670 29.23662 34.36364
## DMCC2126:Stationary:25fold  13.75020 17.95239 28.49754
## DMCC2127:Shaking:100fold    15.79238 18.06204 22.03937
## DMCC2127:Shaking:25fold     11.94225 14.14409 18.38373
## DMCC2127:Stationary:100fold 21.18901 31.73483 36.13001
## DMCC2127:Stationary:25fold  16.98374 23.80439 29.93923
## DMCC2165:Shaking:100fold    17.06819 21.67684 31.48681
## DMCC2165:Shaking:25fold     14.32943 16.96287 19.24541
## DMCC2165:Stationary:100fold 14.61527 26.86130 31.84574
## DMCC2165:Stationary:25fold  16.28243 21.81134 30.38458
## 
## $comparison
## NULL
## 
## $groups
##                             ES5.mod$ES5_chl.tuk groups
## control:Stationary:100fold             35.01510      a
## control:Shaking:100fold                30.92160     ab
## DMCC2127:Stationary:100fold            29.90084     ab
## control:Stationary:25fold              28.23105    abc
## DMCC2126:Stationary:100fold            28.13045    abc
## DMCC2126:Shaking:100fold               26.63321     bc
## control:Shaking:25fold                 26.28822     bc
## DMCC2165:Shaking:100fold               24.56023    bcd
## DMCC2127:Stationary:25fold             24.48688    bcd
## DMCC2165:Stationary:25fold             23.73032    bcd
## DMCC2165:Stationary:100fold            22.51380    cde
## DMCC2126:Stationary:25fold             21.36280    cde
## DMCC2127:Shaking:100fold               18.70574    def
## DMCC2165:Shaking:25fold                18.20998    def
## DMCC2126:Shaking:25fold                16.33068     ef
## DMCC2127:Shaking:25fold                13.80400      f
## 
## attr(,"class")
## [1] "group"

Run analyses for ES13B

Testing variation among potentially resistant cultivars compared to known susceptible cultivars treated with CFCFs from X. necrophora (isolate DMCC 2165) to determine if resistance to direct application of SMs exist.

#Statistical analysis
#####ES13B###
ES13B.chl.anova <- lm (ES13B$chl ~ ES13B$Treatment + 
                         ES13B$HostVariety + 
                         ES13B$isoRepNumber + 
                         ES13B$techRepNumber + 
                         ES13B$SampleNumber)
ES13B.chl.anova
## 
## Call:
## lm(formula = ES13B$chl ~ ES13B$Treatment + ES13B$HostVariety + 
##     ES13B$isoRepNumber + ES13B$techRepNumber + ES13B$SampleNumber)
## 
## Coefficients:
##                 (Intercept)      ES13B$TreatmentDMCC2165  
##                    187.9400                    -105.4678  
##    ES13B$HostVarietyDG47E80     ES13B$HostVarietyDG47X95  
##                     27.8736                      26.3892  
##      ES13B$HostVarietyOsage    ES13B$HostVarietyP5414LLS  
##                     16.1981                      -3.8273  
##   ES13B$isoRepNumberisoRep2    ES13B$isoRepNumberisoRep3  
##                     -7.3121                       1.4292  
## ES13B$techRepNumbertechRep2  ES13B$techRepNumbertechRep3  
##                     29.6658                       8.0253  
##   ES13B$SampleNumbersample2    ES13B$SampleNumbersample3  
##                      0.7302                       1.9473
summary(ES13B.chl.anova)
## 
## Call:
## lm(formula = ES13B$chl ~ ES13B$Treatment + ES13B$HostVariety + 
##     ES13B$isoRepNumber + ES13B$techRepNumber + ES13B$SampleNumber)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -219.035  -47.751   -4.823   42.506  237.651 
## 
## Coefficients:
##                              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                  187.9400    17.1352  10.968   <2e-16 ***
## ES13B$TreatmentDMCC2165     -105.4678     9.9569 -10.592   <2e-16 ***
## ES13B$HostVarietyDG47E80      27.8736    15.5401   1.794   0.0741 .  
## ES13B$HostVarietyDG47X95      26.3892    16.0431   1.645   0.1012    
## ES13B$HostVarietyOsage        16.1981    15.5401   1.042   0.2983    
## ES13B$HostVarietyP5414LLS     -3.8273    15.5401  -0.246   0.8057    
## ES13B$isoRepNumberisoRep2     -7.3121    12.2504  -0.597   0.5511    
## ES13B$isoRepNumberisoRep3      1.4292    12.1499   0.118   0.9065    
## ES13B$techRepNumbertechRep2   29.6658    12.1499   2.442   0.0153 *  
## ES13B$techRepNumbertechRep3    8.0253    12.1499   0.661   0.5095    
## ES13B$SampleNumbersample2      0.7302    12.1733   0.060   0.9522    
## ES13B$SampleNumbersample3      1.9473    12.1733   0.160   0.8730    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 80.75 on 252 degrees of freedom
##   (6 observations deleted due to missingness)
## Multiple R-squared:  0.337,  Adjusted R-squared:  0.308 
## F-statistic: 11.64 on 11 and 252 DF,  p-value: < 2.2e-16
anova(ES13B.chl.anova)
## Analysis of Variance Table
## 
## Response: ES13B$chl
##                      Df  Sum Sq Mean Sq  F value  Pr(>F)    
## ES13B$Treatment       1  745236  745236 114.2939 < 2e-16 ***
## ES13B$HostVariety     4   44757   11189   1.7160 0.14689    
## ES13B$isoRepNumber    2    3558    1779   0.2728 0.76144    
## ES13B$techRepNumber   2   41380   20690   3.1731 0.04355 *  
## ES13B$SampleNumber    2     170      85   0.0131 0.98702    
## Residuals           252 1643127    6520                     
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#Tukey's HSD for Variable chl by Treatment
ES13B.chl.treatment.HSD.test <- HSD.test(ES13B.chl.anova, 'ES13B$Treatment', group = T)
ES13B.chl.treatment.HSD.test
## $statistics
##    MSerror  Df     Mean       CV
##   6520.345 252 160.8255 50.20887
## 
## $parameters
##    test          name.t ntr StudentizedRange alpha
##   Tukey ES13B$Treatment   2         2.785184  0.05
## 
## $means
##          ES13B$chl      std   r Min     Max     Q25     Q50     Q75
## Control   212.7620 79.79142 135   0 402.241 166.577 220.922 257.822
## DMCC2165  106.4733 82.90892 129   0 350.226  51.563  71.243 161.827
## 
## $comparison
## NULL
## 
## $groups
##          ES13B$chl groups
## Control   212.7620      a
## DMCC2165  106.4733      b
## 
## attr(,"class")
## [1] "group"
#Tukey's HSD for Variable chl by Soybean Cultivar
ES13B.chl.host_variety.HSD.test <- HSD.test(ES13B.chl.anova, 'ES13B$HostVariety', group = T)
ES13B.chl.host_variety.HSD.test
## $statistics
##    MSerror  Df     Mean       CV
##   6520.345 252 160.8255 50.20887
## 
## $parameters
##    test            name.t ntr StudentizedRange alpha
##   Tukey ES13B$HostVariety   5         3.885737  0.05
## 
## $means
##          ES13B$chl       std  r Min     Max     Q25      Q50      Q75
## AG4632    146.7014  89.25074 54   0 364.618 68.0405 134.5050 212.5315
## DG47E80   174.5750  94.89959 54   0 372.762 94.9610 203.4325 233.2080
## DG47X95   179.1090  97.67480 48   0 359.307 86.1180 192.7220 255.9690
## Osage     162.8995 111.37700 54   0 402.241 66.9080 155.3500 248.0178
## P5414LLS  142.8741  88.83067 54   0 318.243 59.8260 153.8800 221.6343
## 
## $comparison
## NULL
## 
## $groups
##          ES13B$chl groups
## DG47X95   179.1090      a
## DG47E80   174.5750      a
## Osage     162.8995      a
## AG4632    146.7014      a
## P5414LLS  142.8741      a
## 
## attr(,"class")
## [1] "group"
#Complete ANOVA for ES13B
ES13B.comp.HSD.group <- HSD.test(ES13B.chl.anova, c("ES13B$Treatment", "ES13B$HostVariety"),
                                 group=TRUE,console=TRUE)
## 
## Study: ES13B.chl.anova ~ c("ES13B$Treatment", "ES13B$HostVariety")
## 
## HSD Test for ES13B$chl 
## 
## Mean Square Error:  6520.345 
## 
## ES13B$Treatment:ES13B$HostVariety,  means
## 
##                   ES13B.chl       std  r    Min     Max
## Control:AG4632    190.99715  86.60398 27  0.000 364.618
## Control:DG47E80   228.60578  74.03698 27 99.638 372.762
## Control:DG47X95   217.34011  75.28029 27 62.560 359.307
## Control:Osage     236.66259  98.93830 27  0.000 402.241
## Control:P5414LLS  190.20437  49.79161 27 96.055 269.571
## DMCC2165:AG4632   102.40559  68.28138 27  0.000 279.119
## DMCC2165:DG47E80  120.54422  82.54428 27  0.000 268.043
## DMCC2165:DG47X95  129.95467 102.67650 21  0.000 350.226
## DMCC2165:Osage     89.13633  64.78778 27  0.000 305.544
## DMCC2165:P5414LLS  95.54374  94.62256 27  0.000 318.243
## 
## Alpha: 0.05 ; DF Error: 252 
## Critical Value of Studentized Range: 4.514628 
## 
## Groups according to probability of means differences and alpha level( 0.05 )
## 
## Treatments with the same letter are not significantly different.
## 
##                   ES13B$chl groups
## Control:Osage     236.66259      a
## Control:DG47E80   228.60578      a
## Control:DG47X95   217.34011      a
## Control:AG4632    190.99715     ab
## Control:P5414LLS  190.20437    abc
## DMCC2165:DG47X95  129.95467    bcd
## DMCC2165:DG47E80  120.54422     cd
## DMCC2165:AG4632   102.40559      d
## DMCC2165:P5414LLS  95.54374      d
## DMCC2165:Osage     89.13633      d
ES13B.comp.HSD.group
## $statistics
##    MSerror  Df     Mean       CV
##   6520.345 252 160.8255 50.20887
## 
## $parameters
##    test                            name.t ntr StudentizedRange alpha
##   Tukey ES13B$Treatment:ES13B$HostVariety  10         4.514628  0.05
## 
## $means
##                   ES13B$chl       std  r    Min     Max      Q25     Q50
## Control:AG4632    190.99715  86.60398 27  0.000 364.618 144.0055 209.592
## Control:DG47E80   228.60578  74.03698 27 99.638 372.762 206.6285 227.869
## Control:DG47X95   217.34011  75.28029 27 62.560 359.307 180.9375 220.770
## Control:Osage     236.66259  98.93830 27  0.000 402.241 220.1595 246.824
## Control:P5414LLS  190.20437  49.79161 27 96.055 269.571 163.7070 193.690
## DMCC2165:AG4632   102.40559  68.28138 27  0.000 279.119  55.0810  79.594
## DMCC2165:DG47E80  120.54422  82.54428 27  0.000 268.043  49.0770  93.402
## DMCC2165:DG47X95  129.95467 102.67650 21  0.000 350.226  47.3850  81.525
## DMCC2165:Osage     89.13633  64.78778 27  0.000 305.544  63.5255  67.114
## DMCC2165:P5414LLS  95.54374  94.62256 27  0.000 318.243  35.1075  57.848
##                        Q75
## Control:AG4632    234.2065
## Control:DG47E80   277.7645
## Control:DG47X95   275.6780
## Control:Osage     271.3380
## Control:P5414LLS  228.1255
## DMCC2165:AG4632   128.8945
## DMCC2165:DG47E80  201.5630
## DMCC2165:DG47X95  199.8590
## DMCC2165:Osage     81.3670
## DMCC2165:P5414LLS 102.5945
## 
## $comparison
## NULL
## 
## $groups
##                   ES13B$chl groups
## Control:Osage     236.66259      a
## Control:DG47E80   228.60578      a
## Control:DG47X95   217.34011      a
## Control:AG4632    190.99715     ab
## Control:P5414LLS  190.20437    abc
## DMCC2165:DG47X95  129.95467    bcd
## DMCC2165:DG47E80  120.54422     cd
## DMCC2165:AG4632   102.40559      d
## DMCC2165:P5414LLS  95.54374      d
## DMCC2165:Osage     89.13633      d
## 
## attr(,"class")
## [1] "group"

Same analysis as above using the tukey normalized dataset

#Statistical analysis
#####ES13B.mod###
ES13B.mod.chl.anova <- lm (ES13B.mod$ES13B_chl.tuk ~ ES13B.mod$Treatment + 
                             ES13B.mod$HostVariety + 
                             ES13B.mod$isoRepNumber + 
                             ES13B.mod$techRepNumber + 
                             ES13B.mod$SampleNumber)
ES13B.mod.chl.anova
## 
## Call:
## lm(formula = ES13B.mod$ES13B_chl.tuk ~ ES13B.mod$Treatment + 
##     ES13B.mod$HostVariety + ES13B.mod$isoRepNumber + ES13B.mod$techRepNumber + 
##     ES13B.mod$SampleNumber)
## 
## Coefficients:
##                     (Intercept)      ES13B.mod$TreatmentDMCC2165  
##                         56.4659                         -27.1569  
##    ES13B.mod$HostVarietyDG47E80     ES13B.mod$HostVarietyDG47X95  
##                          6.8552                           6.4268  
##      ES13B.mod$HostVarietyOsage    ES13B.mod$HostVarietyP5414LLS  
##                          3.2278                          -1.2888  
##   ES13B.mod$isoRepNumberisoRep2    ES13B.mod$isoRepNumberisoRep3  
##                         -1.8503                           0.1216  
## ES13B.mod$techRepNumbertechRep2  ES13B.mod$techRepNumbertechRep3  
##                          7.5512                           2.1409  
##   ES13B.mod$SampleNumbersample2    ES13B.mod$SampleNumbersample3  
##                          0.6429                           0.7374
summary(ES13B.mod.chl.anova)
## 
## Call:
## lm(formula = ES13B.mod$ES13B_chl.tuk ~ ES13B.mod$Treatment + 
##     ES13B.mod$HostVariety + ES13B.mod$isoRepNumber + ES13B.mod$techRepNumber + 
##     ES13B.mod$SampleNumber)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -64.139 -11.806   0.251  11.105  58.266 
## 
## Coefficients:
##                                 Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                      56.4659     4.4320  12.740   <2e-16 ***
## ES13B.mod$TreatmentDMCC2165     -27.1569     2.5754 -10.545   <2e-16 ***
## ES13B.mod$HostVarietyDG47E80      6.8552     4.0195   1.705   0.0893 .  
## ES13B.mod$HostVarietyDG47X95      6.4268     4.1496   1.549   0.1227    
## ES13B.mod$HostVarietyOsage        3.2278     4.0195   0.803   0.4227    
## ES13B.mod$HostVarietyP5414LLS    -1.2888     4.0195  -0.321   0.7488    
## ES13B.mod$isoRepNumberisoRep2    -1.8503     3.1686  -0.584   0.5598    
## ES13B.mod$isoRepNumberisoRep3     0.1216     3.1426   0.039   0.9692    
## ES13B.mod$techRepNumbertechRep2   7.5512     3.1426   2.403   0.0170 *  
## ES13B.mod$techRepNumbertechRep3   2.1409     3.1426   0.681   0.4963    
## ES13B.mod$SampleNumbersample2     0.6429     3.1486   0.204   0.8384    
## ES13B.mod$SampleNumbersample3     0.7374     3.1486   0.234   0.8150    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 20.89 on 252 degrees of freedom
##   (6 observations deleted due to missingness)
## Multiple R-squared:  0.3339, Adjusted R-squared:  0.3048 
## F-statistic: 11.48 on 11 and 252 DF,  p-value: < 2.2e-16
anova(ES13B.mod.chl.anova)
## Analysis of Variance Table
## 
## Response: ES13B.mod$ES13B_chl.tuk
##                          Df Sum Sq Mean Sq  F value  Pr(>F)    
## ES13B.mod$Treatment       1  49427   49427 113.3105 < 2e-16 ***
## ES13B.mod$HostVariety     4   2794     698   1.6010 0.17455    
## ES13B.mod$isoRepNumber    2    193      97   0.2216 0.80137    
## ES13B.mod$techRepNumber   2   2663    1331   3.0519 0.04902 *  
## ES13B.mod$SampleNumber    2     28      14   0.0325 0.96804    
## Residuals               252 109925     436                     
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#Tukey's HSD for Variable chl by Treatment
ES13B.mod.chl.treatment.HSD.test <- HSD.test(ES13B.mod.chl.anova, 'ES13B.mod$Treatment',
                                             group = T)
ES13B.mod.chl.treatment.HSD.test
## $statistics
##    MSerror  Df     Mean       CV
##   436.2119 252 49.24912 42.40824
## 
## $parameters
##    test              name.t ntr StudentizedRange alpha
##   Tukey ES13B.mod$Treatment   2         2.785184  0.05
## 
## $means
##          ES13B.mod$ES13B_chl.tuk      std   r Min       Max      Q25      Q50
## Control                 62.62462 19.91280 135   0 104.34627 52.69278 65.58208
## DMCC2165                35.25151 22.09171 129   0  93.72814 21.23548 27.28194
##               Q75
## Control  73.92184
## DMCC2165 51.52459
## 
## $comparison
## NULL
## 
## $groups
##          ES13B.mod$ES13B_chl.tuk groups
## Control                 62.62462      a
## DMCC2165                35.25151      b
## 
## attr(,"class")
## [1] "group"
#Tukey's HSD for Variable chl by Soybean Cultivar
ES13B.mod.chl.host_variety.HSD.test <- HSD.test(ES13B.mod.chl.anova,
                                                'ES13B.mod$HostVariety', group = T)
ES13B.mod.chl.host_variety.HSD.test
## $statistics
##    MSerror  Df     Mean       CV
##   436.2119 252 49.24912 42.40824
## 
## $parameters
##    test                name.t ntr StudentizedRange alpha
##   Tukey ES13B.mod$HostVariety   5         3.885737  0.05
## 
## $means
##          ES13B.mod$ES13B_chl.tuk      std  r Min       Max      Q25      Q50
## AG4632                  46.00199 23.04112 54   0  96.69957 26.32493 44.64489
## DG47E80                 52.85715 24.31248 54   0  98.36929 34.08532 61.52054
## DG47X95                 53.96776 24.74253 48   0  95.60617 31.59813 58.99393
## Osage                   49.22977 28.48843 54   0 104.34627 25.98636 49.51131
## P5414LLS                44.71323 23.92033 54   0  87.02381 23.82148 49.53890
##               Q75
## AG4632   63.64317
## DG47E80  68.39115
## DG47X95  73.50975
## Osage    71.73382
## P5414LLS 65.74587
## 
## $comparison
## NULL
## 
## $groups
##          ES13B.mod$ES13B_chl.tuk groups
## DG47X95                 53.96776      a
## DG47E80                 52.85715      a
## Osage                   49.22977      a
## AG4632                  46.00199      a
## P5414LLS                44.71323      a
## 
## attr(,"class")
## [1] "group"
#Complete ANOVA for ES13B.mod
ES13B.mod.comp.HSD.group <- HSD.test(ES13B.mod.chl.anova, c("ES13B.mod$Treatment", 
                                                            "ES13B.mod$HostVariety"), 
                                                            group=TRUE,console=TRUE)
## 
## Study: ES13B.mod.chl.anova ~ c("ES13B.mod$Treatment", "ES13B.mod$HostVariety")
## 
## HSD Test for ES13B.mod$ES13B_chl.tuk 
## 
## Mean Square Error:  436.2119 
## 
## ES13B.mod$Treatment:ES13B.mod$HostVariety,  means
## 
##                   ES13B.mod.ES13B_chl.tuk      std  r      Min       Max
## Control:AG4632                   57.20904 22.03662 27  0.00000  96.69957
## Control:DG47E80                  66.70288 17.20548 27 35.38176  98.36929
## Control:DG47X95                  64.01404 17.93361 27 24.66777  95.60617
## Control:Osage                    67.15363 26.52535 27  0.00000 104.34627
## Control:P5414LLS                 58.04351 11.99680 27 34.39165  76.51943
## DMCC2165:AG4632                  34.79495 18.32309 27  0.00000  78.61163
## DMCC2165:DG47E80                 39.01143 22.59966 27  0.00000  76.18307
## DMCC2165:DG47X95                 41.05111 26.62931 21  0.00000  93.72814
## DMCC2165:Osage                   31.30592 16.83924 27  0.00000  84.32030
## DMCC2165:P5414LLS                31.38296 25.56143 27  0.00000  87.02381
## 
## Alpha: 0.05 ; DF Error: 252 
## Critical Value of Studentized Range: 4.514628 
## 
## Groups according to probability of means differences and alpha level( 0.05 )
## 
## Treatments with the same letter are not significantly different.
## 
##                   ES13B.mod$ES13B_chl.tuk groups
## Control:Osage                    67.15363      a
## Control:DG47E80                  66.70288      a
## Control:DG47X95                  64.01404      a
## Control:P5414LLS                 58.04351     ab
## Control:AG4632                   57.20904     ab
## DMCC2165:DG47X95                 41.05111     bc
## DMCC2165:DG47E80                 39.01143      c
## DMCC2165:AG4632                  34.79495      c
## DMCC2165:P5414LLS                31.38296      c
## DMCC2165:Osage                   31.30592      c
ES13B.mod.comp.HSD.group
## $statistics
##    MSerror  Df     Mean       CV
##   436.2119 252 49.24912 42.40824
## 
## $parameters
##    test                                    name.t ntr StudentizedRange alpha
##   Tukey ES13B.mod$Treatment:ES13B.mod$HostVariety  10         4.514628  0.05
## 
## $means
##                   ES13B.mod$ES13B_chl.tuk      std  r      Min       Max
## Control:AG4632                   57.20904 22.03662 27  0.00000  96.69957
## Control:DG47E80                  66.70288 17.20548 27 35.38176  98.36929
## Control:DG47X95                  64.01404 17.93361 27 24.66777  95.60617
## Control:Osage                    67.15363 26.52535 27  0.00000 104.34627
## Control:P5414LLS                 58.04351 11.99680 27 34.39165  76.51943
## DMCC2165:AG4632                  34.79495 18.32309 27  0.00000  78.61163
## DMCC2165:DG47E80                 39.01143 22.59966 27  0.00000  76.18307
## DMCC2165:DG47X95                 41.05111 26.62931 21  0.00000  93.72814
## DMCC2165:Osage                   31.30592 16.83924 27  0.00000  84.32030
## DMCC2165:P5414LLS                31.38296 25.56143 27  0.00000  87.02381
##                        Q25      Q50      Q75
## Control:AG4632    47.05388 62.96010 68.61805
## Control:DG47E80   62.26497 67.17475 78.30087
## Control:DG47X95   56.17982 65.54711 77.85944
## Control:Osage     65.40652 71.46612 76.90405
## Control:P5414LLS  51.98765 59.22541 67.22892
## DMCC2165:AG4632   22.34657 29.72913 43.18888
## DMCC2165:DG47E80  20.43572 33.65317 61.08024
## DMCC2165:DG47X95  19.88940 30.28659 60.68214
## DMCC2165:Osage    24.95960 26.04835 30.23822
## DMCC2165:P5414LLS 15.75931 23.21526 36.18682
## 
## $comparison
## NULL
## 
## $groups
##                   ES13B.mod$ES13B_chl.tuk groups
## Control:Osage                    67.15363      a
## Control:DG47E80                  66.70288      a
## Control:DG47X95                  64.01404      a
## Control:P5414LLS                 58.04351     ab
## Control:AG4632                   57.20904     ab
## DMCC2165:DG47X95                 41.05111     bc
## DMCC2165:DG47E80                 39.01143      c
## DMCC2165:AG4632                  34.79495      c
## DMCC2165:P5414LLS                31.38296      c
## DMCC2165:Osage                   31.30592      c
## 
## attr(,"class")
## [1] "group"

Run analyses for ES14A

This dataset contains chlorophyll content measured among plant species treated with CFCFs from X. necrophora (isolate DMCC 2165) to estimate the specificy of SMs.

#####ES14A###
ES14A.chl.anova <- lm (ES14A$chl ~ ES14A$Treatment + 
                         ES14A$Host + ES14A$isoRepNumber + 
                         ES14A$techRepNumber + 
                         ES14A$LeafSampleNumber)
ES14A.chl.anova
## 
## Call:
## lm(formula = ES14A$chl ~ ES14A$Treatment + ES14A$Host + ES14A$isoRepNumber + 
##     ES14A$techRepNumber + ES14A$LeafSampleNumber)
## 
## Coefficients:
##                   (Intercept)        ES14A$TreatmentDMCC2165  
##                       204.803                        -39.317  
##              ES14A$HostPeanut              ES14A$HostSoybean  
##                        71.821                        -20.797  
##              ES14A$HostTomato      ES14A$isoRepNumberisoRep2  
##                        20.597                          8.076  
##     ES14A$isoRepNumberisoRep3    ES14A$techRepNumbertechRep2  
##                        10.061                         -3.623  
##   ES14A$techRepNumbertechRep3  ES14A$LeafSampleNumbersample2  
##                        -2.447                         -2.221  
## ES14A$LeafSampleNumbersample3  
##                       -17.082
summary(ES14A.chl.anova)
## 
## Call:
## lm(formula = ES14A$chl ~ ES14A$Treatment + ES14A$Host + ES14A$isoRepNumber + 
##     ES14A$techRepNumber + ES14A$LeafSampleNumber)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -152.26  -25.67    3.28   28.37  140.22 
## 
## Coefficients:
##                               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                    204.803     11.492  17.821  < 2e-16 ***
## ES14A$TreatmentDMCC2165        -39.317      6.956  -5.652 5.34e-08 ***
## ES14A$HostPeanut                71.821      9.760   7.359 4.58e-12 ***
## ES14A$HostSoybean              -20.797      9.760  -2.131   0.0343 *  
## ES14A$HostTomato                20.597      9.914   2.078   0.0390 *  
## ES14A$isoRepNumberisoRep2        8.076      8.552   0.944   0.3461    
## ES14A$isoRepNumberisoRep3       10.061      8.452   1.190   0.2353    
## ES14A$techRepNumbertechRep2     -3.623      8.552  -0.424   0.6723    
## ES14A$techRepNumbertechRep3     -2.447      8.552  -0.286   0.7751    
## ES14A$LeafSampleNumbersample2   -2.221      8.512  -0.261   0.7944    
## ES14A$LeafSampleNumbersample3  -17.082      8.512  -2.007   0.0461 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 50.71 on 202 degrees of freedom
##   (3 observations deleted due to missingness)
## Multiple R-squared:  0.4051, Adjusted R-squared:  0.3756 
## F-statistic: 13.75 on 10 and 202 DF,  p-value: < 2.2e-16
anova(ES14A.chl.anova)
## Analysis of Variance Table
## 
## Response: ES14A$chl
##                         Df Sum Sq Mean Sq F value    Pr(>F)    
## ES14A$Treatment          1  81494   81494 31.6869 6.003e-08 ***
## ES14A$Host               3 255475   85158 33.1116 < 2.2e-16 ***
## ES14A$isoRepNumber       2   4050    2025  0.7874    0.4564    
## ES14A$techRepNumber      2    478     239  0.0930    0.9112    
## ES14A$LeafSampleNumber   2  12250    6125  2.3815    0.0950 .  
## Residuals              202 519515    2572                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#Tukey's HSD for Variable chl by Treatment
ES14A.chl.treatment.HSD.test <- HSD.test(ES14A.chl.anova, 'ES14A$Treatment', group = T)
ES14A.chl.treatment.HSD.test
## $statistics
##    MSerror  Df     Mean       CV
##   2571.854 202 200.2661 25.32304
## 
## $parameters
##    test          name.t ntr StudentizedRange alpha
##   Tukey ES14A$Treatment   2         2.788514  0.05
## 
## $means
##          ES14A$chl      std   r    Min     Max      Q25     Q50      Q75
## Control   220.1037 48.30845 105 74.284 312.775 199.7190 220.323 244.5180
## DMCC2165  180.9794 71.63395 108 43.371 317.520 136.5077 190.138 227.8515
## 
## $comparison
## NULL
## 
## $groups
##          ES14A$chl groups
## Control   220.1037      a
## DMCC2165  180.9794      b
## 
## attr(,"class")
## [1] "group"
#Tukey's HSD for Variable chl by Plant Species
ES14A.chl.host.HSD.test <- HSD.test(ES14A.chl.anova, 'ES14A$Host', group = T)
ES14A.chl.host.HSD.test
## $statistics
##    MSerror  Df     Mean       CV
##   2571.854 202 200.2661 25.32304
## 
## $parameters
##    test     name.t ntr StudentizedRange alpha
##   Tukey ES14A$Host   4         3.663584  0.05
## 
## $means
##         ES14A$chl      std  r     Min     Max      Q25      Q50      Q75
## Cotton   182.7328 41.22083 54  99.321 258.986 151.8048 189.3455 208.6360
## Peanut   254.5536 39.15515 54 104.832 317.520 232.0955 254.8250 282.4742
## Soybean  161.9354 88.07831 54  43.371 312.775  66.5095 174.5450 226.9425
## Tomato   201.9352 26.66869 51 117.923 244.624 187.7870 203.6790 219.5155
## 
## $comparison
## NULL
## 
## $groups
##         ES14A$chl groups
## Peanut   254.5536      a
## Tomato   201.9352      b
## Cotton   182.7328     bc
## Soybean  161.9354      c
## 
## attr(,"class")
## [1] "group"
#Complete ANOVA for ES14A
ES14A.comp.HSD.group <- HSD.test(ES14A.chl.anova, c("ES14A$Treatment", "ES14A$Host"), group=TRUE,console=TRUE)
## 
## Study: ES14A.chl.anova ~ c("ES14A$Treatment", "ES14A$Host")
## 
## HSD Test for ES14A$chl 
## 
## Mean Square Error:  2571.854 
## 
## ES14A$Treatment:ES14A$Host,  means
## 
##                  ES14A.chl      std  r     Min     Max
## Control:Cotton   194.11622 42.12477 27 106.098 254.411
## Control:Peanut   243.47885 43.34219 27 104.832 305.065
## Control:Soybean  226.62589 63.78820 27  74.284 312.775
## Control:Tomato   215.70517 17.85696 24 183.593 244.624
## DMCC2165:Cotton  171.34937 37.68338 27  99.321 258.986
## DMCC2165:Peanut  265.62833 31.49505 27 200.016 317.520
## DMCC2165:Soybean  97.24481 55.25735 27  43.371 210.220
## DMCC2165:Tomato  189.69526 27.47809 27 117.923 236.489
## 
## Alpha: 0.05 ; DF Error: 202 
## Critical Value of Studentized Range: 4.331714 
## 
## Groups according to probability of means differences and alpha level( 0.05 )
## 
## Treatments with the same letter are not significantly different.
## 
##                  ES14A$chl groups
## DMCC2165:Peanut  265.62833      a
## Control:Peanut   243.47885     ab
## Control:Soybean  226.62589    abc
## Control:Tomato   215.70517     bc
## Control:Cotton   194.11622     cd
## DMCC2165:Tomato  189.69526     cd
## DMCC2165:Cotton  171.34937      d
## DMCC2165:Soybean  97.24481      e
ES14A.comp.HSD.group
## $statistics
##    MSerror  Df     Mean       CV
##   2571.854 202 200.2661 25.32304
## 
## $parameters
##    test                     name.t ntr StudentizedRange alpha
##   Tukey ES14A$Treatment:ES14A$Host   8         4.331714  0.05
## 
## $means
##                  ES14A$chl      std  r     Min     Max      Q25      Q50
## Control:Cotton   194.11622 42.12477 27 106.098 254.411 172.7065 201.2180
## Control:Peanut   243.47885 43.34219 27 104.832 305.065 220.0160 244.4330
## Control:Soybean  226.62589 63.78820 27  74.284 312.775 205.7205 227.9410
## Control:Tomato   215.70517 17.85696 24 183.593 244.624 203.4402 214.3875
## DMCC2165:Cotton  171.34937 37.68338 27  99.321 258.986 146.1180 180.5490
## DMCC2165:Peanut  265.62833 31.49505 27 200.016 317.520 247.1435 262.9750
## DMCC2165:Soybean  97.24481 55.25735 27  43.371 210.220  52.2970  66.4980
## DMCC2165:Tomato  189.69526 27.47809 27 117.923 236.489 178.0980 191.1460
##                       Q75
## Control:Cotton   229.7960
## Control:Peanut   274.2060
## Control:Soybean  274.5295
## Control:Tomato   227.4280
## DMCC2165:Cotton  198.6270
## DMCC2165:Peanut  290.1215
## DMCC2165:Soybean 143.0605
## DMCC2165:Tomato  206.2940
## 
## $comparison
## NULL
## 
## $groups
##                  ES14A$chl groups
## DMCC2165:Peanut  265.62833      a
## Control:Peanut   243.47885     ab
## Control:Soybean  226.62589    abc
## Control:Tomato   215.70517     bc
## Control:Cotton   194.11622     cd
## DMCC2165:Tomato  189.69526     cd
## DMCC2165:Cotton  171.34937      d
## DMCC2165:Soybean  97.24481      e
## 
## attr(,"class")
## [1] "group"

Same analysis as above using Tukey’s normalized data.

#####ES14A.mod.mod###
ES14A.mod.chl.anova <- lm (ES14A.mod$ES14A_chl.tuk ~ ES14A.mod$Treatment + 
                             ES14A.mod$Host + 
                             ES14A.mod$isoRepNumber + 
                             ES14A.mod$techRepNumber + 
                             ES14A.mod$LeafSampleNumber)
ES14A.mod.chl.anova
## 
## Call:
## lm(formula = ES14A.mod$ES14A_chl.tuk ~ ES14A.mod$Treatment + 
##     ES14A.mod$Host + ES14A.mod$isoRepNumber + ES14A.mod$techRepNumber + 
##     ES14A.mod$LeafSampleNumber)
## 
## Coefficients:
##                       (Intercept)        ES14A.mod$TreatmentDMCC2165  
##                           9573.32                           -2709.06  
##              ES14A.mod$HostPeanut              ES14A.mod$HostSoybean  
##                           6109.14                            -562.35  
##              ES14A.mod$HostTomato      ES14A.mod$isoRepNumberisoRep2  
##                           1457.89                             752.80  
##     ES14A.mod$isoRepNumberisoRep3    ES14A.mod$techRepNumbertechRep2  
##                            707.59                            -175.15  
##   ES14A.mod$techRepNumbertechRep3  ES14A.mod$LeafSampleNumbersample2  
##                           -380.75                             -57.52  
## ES14A.mod$LeafSampleNumbersample3  
##                           -831.24
summary(ES14A.mod.chl.anova)
## 
## Call:
## lm(formula = ES14A.mod$ES14A_chl.tuk ~ ES14A.mod$Treatment + 
##     ES14A.mod$Host + ES14A.mod$isoRepNumber + ES14A.mod$techRepNumber + 
##     ES14A.mod$LeafSampleNumber)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -11413  -2124     40   2186  11598 
## 
## Coefficients:
##                                   Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                        9573.32     861.86  11.108  < 2e-16 ***
## ES14A.mod$TreatmentDMCC2165       -2709.06     521.66  -5.193 5.03e-07 ***
## ES14A.mod$HostPeanut               6109.14     731.96   8.346 1.10e-14 ***
## ES14A.mod$HostSoybean              -562.35     731.96  -0.768   0.4432    
## ES14A.mod$HostTomato               1457.89     743.48   1.961   0.0513 .  
## ES14A.mod$isoRepNumberisoRep2       752.80     641.39   1.174   0.2419    
## ES14A.mod$isoRepNumberisoRep3       707.59     633.89   1.116   0.2656    
## ES14A.mod$techRepNumbertechRep2    -175.15     641.39  -0.273   0.7851    
## ES14A.mod$techRepNumbertechRep3    -380.75     641.39  -0.594   0.5534    
## ES14A.mod$LeafSampleNumbersample2   -57.52     638.34  -0.090   0.9283    
## ES14A.mod$LeafSampleNumbersample3  -831.24     638.34  -1.302   0.1943    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 3803 on 202 degrees of freedom
##   (3 observations deleted due to missingness)
## Multiple R-squared:  0.3985, Adjusted R-squared:  0.3687 
## F-statistic: 13.38 on 10 and 202 DF,  p-value: < 2.2e-16
anova(ES14A.mod.chl.anova)
## Analysis of Variance Table
## 
## Response: ES14A.mod$ES14A_chl.tuk
##                             Df     Sum Sq   Mean Sq F value    Pr(>F)    
## ES14A.mod$Treatment          1  389423237 389423237 26.9209 5.141e-07 ***
## ES14A.mod$Host               3 1485413072 495137691 34.2289 < 2.2e-16 ***
## ES14A.mod$isoRepNumber       2   25123911  12561955  0.8684    0.4212    
## ES14A.mod$techRepNumber      2    5115841   2557921  0.1768    0.8381    
## ES14A.mod$LeafSampleNumber   2   30598645  15299322  1.0576    0.3492    
## Residuals                  202 2922025050  14465471                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#Tukey's HSD for Variable chl by Treatment
ES14A.mod.chl.treatment.HSD.test <- HSD.test(ES14A.mod.chl.anova, 'ES14A.mod$Treatment', group = T)
ES14A.mod.chl.treatment.HSD.test
## $statistics
##    MSerror  Df     Mean       CV
##   14465471 202 9953.906 38.20962
## 
## $parameters
##    test              name.t ntr StudentizedRange alpha
##   Tukey ES14A.mod$Treatment   2         2.788514  0.05
## 
## $means
##          ES14A.mod$ES14A_chl.tuk      std   r       Min      Max      Q25
## Control                11325.224 3958.923 105 1687.6965 20150.01 9294.444
## DMCC2165                8620.679 5150.335 108  667.0663 20680.22 4821.441
##                Q50      Q75
## Control  11009.769 13177.60
## DMCC2165  8538.763 11666.87
## 
## $comparison
## NULL
## 
## $groups
##          ES14A.mod$ES14A_chl.tuk groups
## Control                11325.224      a
## DMCC2165                8620.679      b
## 
## attr(,"class")
## [1] "group"
#Tukey's HSD for Variable chl by Plant Species
ES14A.mod.chl.host.HSD.test <- HSD.test(ES14A.mod.chl.anova, 'ES14A.mod$Host', group = T)
ES14A.mod.chl.host.HSD.test
## $statistics
##    MSerror  Df     Mean       CV
##   14465471 202 9953.906 38.20962
## 
## $parameters
##    test         name.t ntr StudentizedRange alpha
##   Tukey ES14A.mod$Host   4         3.663584  0.05
## 
## $means
##         ES14A.mod$ES14A_chl.tuk      std  r       Min      Max       Q25
## Cotton                 8224.039 3055.399 54 2785.4532 14551.29  5790.517
## Peanut                14333.182 3553.092 54 3057.3977 20680.22 12044.169
## Soybean                7661.688 6130.187 54  667.0663 20150.01  1394.679
## Tomato                 9575.703 2068.306 51 3745.4797 13187.45  8357.435
##               Q50      Q75
## Cotton   8477.450 10021.99
## Peanut  14150.559 16902.50
## Soybean  7366.996 11586.99
## Tomato   9614.624 10940.26
## 
## $comparison
## NULL
## 
## $groups
##         ES14A.mod$ES14A_chl.tuk groups
## Peanut                14333.182      a
## Tomato                 9575.703      b
## Cotton                 8224.039      b
## Soybean                7661.688      b
## 
## attr(,"class")
## [1] "group"
#Complete ANOVA for ES14A.mod
ES14A.mod.comp.HSD.group <- HSD.test(ES14A.mod.chl.anova, c("ES14A.mod$Treatment",
                                                            "ES14A.mod$Host"),
                                                            group=TRUE,console=TRUE)
## 
## Study: ES14A.mod.chl.anova ~ c("ES14A.mod$Treatment", "ES14A.mod$Host")
## 
## HSD Test for ES14A.mod$ES14A_chl.tuk 
## 
## Mean Square Error:  14465471 
## 
## ES14A.mod$Treatment:ES14A.mod$Host,  means
## 
##                  ES14A.mod.ES14A_chl.tuk      std  r       Min      Max
## Control:Cotton                  9103.740 3172.165 27 3121.3676 14110.73
## Control:Peanut                 13336.356 3754.679 27 3057.3977 19300.87
## Control:Soybean                12128.331 5109.049 27 1687.6965 20150.01
## Control:Tomato                 10658.376 1512.759 24 8038.0704 13187.45
## DMCC2165:Cotton                 7344.338 2712.946 27 2785.4532 14551.29
## DMCC2165:Peanut                15330.007 3094.045 27 9318.2997 20680.22
## DMCC2165:Soybean                3195.045 3010.793 27  667.0663 10153.43
## DMCC2165:Tomato                 8613.327 2039.245 27 3745.4797 12440.10
## 
## Alpha: 0.05 ; DF Error: 202 
## Critical Value of Studentized Range: 4.331714 
## 
## Groups according to probability of means differences and alpha level( 0.05 )
## 
## Treatments with the same letter are not significantly different.
## 
##                  ES14A.mod$ES14A_chl.tuk groups
## DMCC2165:Peanut                15330.007      a
## Control:Peanut                 13336.356     ab
## Control:Soybean                12128.331     bc
## Control:Tomato                 10658.376    bcd
## Control:Cotton                  9103.740    cde
## DMCC2165:Tomato                 8613.327     de
## DMCC2165:Cotton                 7344.338      e
## DMCC2165:Soybean                3195.045      f
ES14A.mod.comp.HSD.group
## $statistics
##    MSerror  Df     Mean       CV
##   14465471 202 9953.906 38.20962
## 
## $parameters
##    test                             name.t ntr StudentizedRange alpha
##   Tukey ES14A.mod$Treatment:ES14A.mod$Host   8         4.331714  0.05
## 
## $means
##                  ES14A.mod$ES14A_chl.tuk      std  r       Min      Max
## Control:Cotton                  9103.740 3172.165 27 3121.3676 14110.73
## Control:Peanut                 13336.356 3754.679 27 3057.3977 19300.87
## Control:Soybean                12128.331 5109.049 27 1687.6965 20150.01
## Control:Tomato                 10658.376 1512.759 24 8038.0704 13187.45
## DMCC2165:Cotton                 7344.338 2712.946 27 2785.4532 14551.29
## DMCC2165:Peanut                15330.007 3094.045 27 9318.2997 20680.22
## DMCC2165:Soybean                3195.045 3010.793 27  667.0663 10153.43
## DMCC2165:Tomato                 8613.327 2039.245 27 3745.4797 12440.10
##                         Q25       Q50       Q75
## Control:Cotton    7235.4241  9415.107 11840.543
## Control:Peanut   10983.3242 13169.695 16057.689
## Control:Soybean   9787.4152 11674.646 16090.596
## Control:Tomato    9595.2157 10503.255 11629.391
## DMCC2165:Cotton   5421.6757  7809.559  9207.086
## DMCC2165:Peanut  13422.7747 14940.064 17699.067
## DMCC2165:Soybean   921.2485  1394.263  5240.336
## DMCC2165:Tomato   7629.0246  8616.978  9828.773
## 
## $comparison
## NULL
## 
## $groups
##                  ES14A.mod$ES14A_chl.tuk groups
## DMCC2165:Peanut                15330.007      a
## Control:Peanut                 13336.356     ab
## Control:Soybean                12128.331     bc
## Control:Tomato                 10658.376    bcd
## Control:Cotton                  9103.740    cde
## DMCC2165:Tomato                 8613.327     de
## DMCC2165:Cotton                 7344.338      e
## DMCC2165:Soybean                3195.045      f
## 
## attr(,"class")
## [1] "group"

Plotting individual plots and composite figures

Individual plots for figure 1

Extract the information needed for panel “A”

##Extract all control (ES5: 7 DOE)
ES5.control <- subset(ES5.mod, Treatment== "control")
ES5.Xn <- subset(ES5.mod, Treatment== c("DMCC2126", "DMCC2127", "DMCC2165"))

ES5.control <- ES5.control %>%
  add_column(Species = "control")

ES5.Xn <- ES5.Xn %>%
  add_column(Species = "X.necrophora")

ES5.mod.v2 <- rbind(ES5.control, ES5.Xn)


ES5.mod.ggplot <- ggplot(ES5.mod.v2, aes(x = reorder(Species, -chl, na.rm = TRUE), 
                                         y = chl, fill = Species)) + 
  geom_boxplot() + #geom_point(aes(colour = factor(LeafSampleNumber)))# + geom_jitter()
  #scale_fill_grey(start = 1, end = 0.4) + labs(tag = "A") +
  scale_fill_manual(values = c("#FFFFFF", "#545454"))+ labs(tag = "A") +
  xlab("Treatment") + ylab("Total Chlorophyll (ng/sq mm)") + 
  theme(plot.title = element_text(size = 12, hjust = 0.1, face = "bold"),
        axis.title.x = element_text(size=10, face = "bold"), 
        axis.title.y = element_text(size = 10, face = "bold"), 
        axis.text.x = element_text(angle = 45, hjust = 1)) + 
  theme(panel.border = element_blank(), panel.grid.major = element_blank(), 
        panel.grid.minor = element_blank(), axis.line = element_line(colour = "black")) + 
  facet_wrap(~ Dilution)
ES5.mod.ggplot
## Warning: Removed 8 rows containing non-finite values (stat_boxplot).

Individual plot for panel B

##Extract all control (ES2), colletrichum, and X. necrophora
ES2.control <- subset(ES2.mod, Treatment== "control")
ES2.coll <- subset(ES2.mod, Treatment== "DMCC2966")
ES2.Xn <- subset(ES2.mod, Treatment== c("DMCC2126", "DMCC2127", "DMCC2165"))

ES2.control <- ES2.control %>%
  add_column(Species = "control")

ES2.coll <- ES2.coll %>%
  add_column(Species = "C.siamense")

ES2.Xn <- ES2.Xn %>%
  add_column(Species = "X.necrophora")

ES2.mod.v2 <- rbind(ES2.control, ES2.coll, ES2.Xn)

#plot for figure by species by dilution factor

#Reorganizing for plotting

ES2.mod.v2$Species <- factor(ES2.mod.v2$Species,                 
                         levels = c("control", "C.siamense", "X.necrophora"))

ES2.mod.v2.ggplot <- ggplot(ES2.mod.v2, aes(x = reorder(Species, -chl, na.rm = TRUE),
                                            y = chl, fill = Species)) + geom_boxplot() + #geom_point(aes(colour = factor(LeafSampleNumber)))# + geom_jitter()
 # scale_fill_grey("control" = 1, "C.siamense" =0.7, "X.necrophora"= 0.4) 
  scale_fill_manual(values = c("#FFFFFF", "#AAAAAA", "#545454"))+ labs(tag = "B") +
  xlab("Treatment") + ylab("Total Chlorophyll (ng/sq mm)") + 
  theme(plot.title = element_text(size = 12, hjust = 0.1, face = "bold"), 
        axis.title.x = element_text(size=10, face = "bold"), 
        axis.title.y = element_text(size = 10, face = "bold"), 
        axis.text.x = element_text(angle = 45, hjust = 1)) + 
  theme(panel.border = element_blank(), 
        panel.grid.major = element_blank(), 
        panel.grid.minor = element_blank(), 
        axis.line = element_line(colour = "black")) + 
  facet_wrap(~ Dilution)
ES2.mod.v2.ggplot
## Warning: Removed 32 rows containing non-finite values (stat_boxplot).

Plot composite figure 1

###Grid for composite figure 1 (updated 10/25/2021). Using ES2 and ES5 only.
gridExtra::grid.arrange(ES5.mod.ggplot, ES2.mod.v2.ggplot, ncol=2)
## Warning: Removed 8 rows containing non-finite values (stat_boxplot).
## Warning: Removed 32 rows containing non-finite values (stat_boxplot).

Plotting individual plots and composite figure 3

Individual panels A, B, C, and D.

###Plot HostVariety only w/ outliers
ES13B.ByHosCult <- ggplot(ES13B.mod, aes(x = reorder(HostVariety, -chl, na.rm = TRUE), 
                                         y = chl, fill=HostVariety)) + 
  geom_boxplot() +
  scale_fill_grey(start = 1, end = 0.4) + labs(tag = "A") + 
  xlab("Soybean Cultivar") + ylab("Total Chlorophyll (ng/sq mm)") + 
  theme(plot.title = element_text(size = 12, hjust = 0.1, face = "bold"), 
        axis.title.x = element_text(size=10, face = "bold"), 
        axis.title.y = element_text(size = 10, face = "bold"), 
        axis.text.x = element_text(angle = 45, hjust = 1)) + 
  theme(panel.border = element_blank(), 
        panel.grid.major = element_blank(), 
        panel.grid.minor = element_blank(), 
        axis.line = element_line(colour = "black")) 
ES13B.ByHosCult
## Warning: Removed 6 rows containing non-finite values (stat_boxplot).

Individual panel B

###Plot by variety by treatment w/ outliers
ES13B.ggplot.ByCultByTreat <- ggplot(ES13B.mod, aes(x = reorder(HostVariety, -chl, 
                                                                na.rm = TRUE), 
                                                    y = chl, fill=Treatment)) +
  geom_boxplot() + #+ geom_point(aes(colour = factor(LeafSampleNumber)))# + geom_jitter()
    scale_fill_grey(start = 1, end = 0.4) + labs(tag = "B") + 
    xlab("Soybean Cultivar") + ylab("Total Chlorophyll (ng/sq mm)") + 
    theme(plot.title = element_text(size = 12, hjust = 0.1, face = "bold"), 
          axis.title.x = element_text(size=10, face = "bold"), 
          axis.title.y =element_text(size = 10, face = "bold"), 
          axis.text.x = element_text(angle = 45, hjust = 1)) + 
    theme(panel.border = element_blank(), 
          panel.grid.major = element_blank(), 
          panel.grid.minor = element_blank(), 
          axis.line = element_line(colour = "black")) 
ES13B.ggplot.ByCultByTreat
## Warning: Removed 6 rows containing non-finite values (stat_boxplot).

Panel C

###Plot By Host only w/ outliers for grid
ES14A.ggplot.ByHost <- ggplot(ES14A.mod, aes(x = reorder(Host, -chl, na.rm = TRUE), 
                                             y = chl, fill=Host)) + 
  geom_boxplot() + #+ geom_point(aes(colour = factor(LeafSampleNumber)))# + geom_jitter()
    scale_fill_grey(start = 1, end = 0.4) + labs(tag = "C") + 
    xlab("Plant Species") + ylab("Total Chlorophyll (ng/sq mm)") + 
    theme(plot.title = element_text(size = 12, hjust = 0.1, face = "bold"), 
          axis.title.x = element_text(size=10, face = "bold"), 
          axis.title.y = element_text(size = 10, face = "bold"), 
          axis.text.x = element_text(angle = 45, hjust = 1)) + 
    theme(panel.border = element_blank(), 
          panel.grid.major = element_blank(), 
          panel.grid.minor = element_blank(), 
          axis.line = element_line(colour = "black")) 
ES14A.ggplot.ByHost 
## Warning: Removed 3 rows containing non-finite values (stat_boxplot).

Panel D

###Plot by host by treatment w/ outliers
ES14A.ggplot.ByHostByTreat <- ggplot(ES14A.mod, aes(x = reorder(Host, -chl, na.rm = TRUE),
                                                    y = chl, fill=Treatment)) +
  geom_boxplot() + #+ geom_point(aes(colour = factor(LeafSampleNumber)))# + geom_jitter()
  scale_fill_grey(start = 1, end = 0.4) + labs(tag = "D") +
    xlab("Plant Species") + ylab("Total Chlorophyll (ng/sq mm)") + 
    theme(plot.title = element_text(size = 12, hjust = 0.1, face = "bold"), 
          axis.title.x = element_text(size=10, face = "bold"), 
          axis.title.y = element_text(size = 10, face = "bold"), 
          axis.text.x = element_text(angle = 45, hjust = 1)) + 
    theme(panel.border = element_blank(), 
          panel.grid.major = element_blank(), panel.grid.minor = element_blank(), 
          axis.line = element_line(colour = "black")) 
ES14A.ggplot.ByHostByTreat 
## Warning: Removed 3 rows containing non-finite values (stat_boxplot).

Composite figure 3

###Grid for composite figure 3 (08/16/2021). Using ES13B and ES14 only.
gridExtra::grid.arrange(ES13B.ByHosCult,
                        ES13B.ggplot.ByCultByTreat , 
                        ES14A.ggplot.ByHost, 
                        ES14A.ggplot.ByHostByTreat, ncol=2)
## Warning: Removed 6 rows containing non-finite values (stat_boxplot).

## Warning: Removed 6 rows containing non-finite values (stat_boxplot).
## Warning: Removed 3 rows containing non-finite values (stat_boxplot).

## Warning: Removed 3 rows containing non-finite values (stat_boxplot).

Supplementary Materials/Figures

Plotting Supplementary Figure 1

#ES2 by treatment by dilution, by growth conditions no title
ES2.mod.ggplot.v2 <- ggplot(ES2.mod, aes(x = reorder(Treatment, -chl, na.rm = TRUE), 
                                         y = chl, fill = Dilution)) + 
  geom_boxplot() + #geom_point(aes(colour = factor(LeafSampleNumber)))# + geom_jitter()
  scale_fill_grey(start = 1, end = 0.4) + labs(tag = "A") +
  xlab("Treatment") + ylab("Total Chlorophyll (ng/sq mm)") + 
  theme(plot.title = element_text(size = 12, hjust = 0.1, face = "bold"), 
        axis.title.x = element_text(size=10, face = "bold"),
        axis.title.y = element_text(size = 10, face = "bold"), 
        axis.text.x = element_text(angle = 45, hjust = 1)) + 
  theme(panel.border = element_blank(), 
        panel.grid.major = element_blank(), 
        panel.grid.minor = element_blank(), 
        axis.line = element_line(colour = "black")) 
ES2.mod.ggplot.v2
## Warning: Removed 60 rows containing non-finite values (stat_boxplot).

#ES5 by treatment by dilution, no title
ES5.mod.ggplot.v2 <- ggplot(ES5.mod, aes(x = reorder(Treatment, -chl, na.rm = TRUE), 
                                         y = chl, fill = Dilution)) + 
  geom_boxplot() + #geom_point(aes(colour = factor(LeafSampleNumber)))# + geom_jitter()
  scale_fill_grey(start = 1, end = 0.4) + labs(tag = "B") +
  xlab("Treatment") + ylab("Total Chlorophyll (ng/sq mm)") + 
  theme(plot.title = element_text(size = 12, hjust = 0.1, face = "bold"),
        axis.title.x = element_text(size=10, face = "bold"), 
        axis.title.y = element_text(size = 10, face = "bold"), 
        axis.text.x = element_text(angle = 45, hjust = 1)) + 
  theme(panel.border = element_blank(), 
        panel.grid.major = element_blank(), 
        panel.grid.minor = element_blank(), 
        axis.line = element_line(colour = "black")) + 
  facet_wrap(~ Condition)
ES5.mod.ggplot.v2
## Warning: Removed 12 rows containing non-finite values (stat_boxplot).

#ES5 by conditions (side by side)
ES5.mod.ggplot.v3 <- ggplot(ES5.mod, aes(x = reorder(Condition, -chl, na.rm = TRUE), 
                                         y = chl, fill=Treatment)) + 
  geom_boxplot() +
  scale_fill_grey(start =1, end = 0.4) + labs(tag = "C") +
  xlab("Growth Condition") + ylab("Total Chlorophyll (ng/sq mm)") + 
  theme(plot.title = element_text(size = 12, hjust = 0.1, face = "bold"), 
        axis.title.x = element_text(size=10, face = "bold"), 
        axis.title.y = element_text(size = 10, face = "bold"), 
        axis.text.x = element_text(angle = 45, hjust = 1)) + 
  theme(panel.border = element_blank(), 
        panel.grid.major = element_blank(), 
        panel.grid.minor = element_blank(), 
        axis.line = element_line(colour = "black")) 
ES5.mod.ggplot.v3 
## Warning: Removed 12 rows containing non-finite values (stat_boxplot).

#ES5 by dilutions (side by side)
ES5.mod.ggplot.v4 <- ggplot(ES5.mod, aes(x = reorder(Dilution, -chl, na.rm = TRUE), 
                                         y = chl, fill=Treatment)) + 
  geom_boxplot() +
  scale_fill_grey(start =1, end = 0.4) + labs(tag = "D") +
  xlab("Dilution Factor") + ylab("Total Chlorophyll (ng/sq mm)") + 
  theme(plot.title = element_text(size = 12, hjust = 0.1, face = "bold"), 
        axis.title.x = element_text(size=10, face = "bold"), 
        axis.title.y = element_text(size = 10, face = "bold"), 
        axis.text.x = element_text(angle = 45, hjust = 1)) + 
  theme(panel.border = element_blank(), 
        panel.grid.major = element_blank(), 
        panel.grid.minor = element_blank(), 
        axis.line = element_line(colour = "black")) 
ES5.mod.ggplot.v4
## Warning: Removed 12 rows containing non-finite values (stat_boxplot).

###Grid for supplementary figure 1 (updated 08/25/2021). Using ES2 and ES5 only.
gridExtra::grid.arrange(ES2.mod.ggplot.v2, 
                        ES5.mod.ggplot.v2, 
                        ES5.mod.ggplot.v3,ES5.mod.ggplot.v4, ncol=2)
## Warning: Removed 60 rows containing non-finite values (stat_boxplot).
## Warning: Removed 12 rows containing non-finite values (stat_boxplot).

## Warning: Removed 12 rows containing non-finite values (stat_boxplot).

## Warning: Removed 12 rows containing non-finite values (stat_boxplot).

Plotting Supplementary Figure 2

This composite figure contained validation chlorophyll content (chemical vs digital extractions) on panel A, fungal biomass based on Whatmat No 1 filter weight on panel B, measurements of pH from initial potato dextrose broth and filtered stock cell-free culture filtrates (CFCFs) on panel C, and the pearson correlation between final pH and digital chlorophyll content on panel D.

Loading datasets for composite figure

#Load datasets
ES10.chem <- read.csv("../raw_data/ES10.chem.chl.csv", header = T)
#Chlorophyll content obtained chemically for a dataset with all biomass and pH measurements

ES10.digital <- read.csv("../raw_data/ES10.digital.chl.csv", header = T)
#Chlorophyll content obtained digitally for a dataset with all biomass and pH measurements

BiomassAndpH.metadata <- read.csv("../raw_data/FilteringTreatments.metadata.csv", 
                                  header = T)

Summarizing and aggregating datasets

#Obtaining sums for ES10  because digital measurements=3 per experimental unit,
#but chemical measurements=1 per experimental unit. 

ES10.digital.sum <- aggregate(ES10.digital$chl,list(ES10.digital$ExpCode),sum)

names(ES10.digital.sum)[names(ES10.digital.sum) == "x"] <- "dig.chl"

#Merging ES10 chem and ES10 digital
ES10.chem.dig = merge(ES10.chem, ES10.digital.sum, by.x='ExpCode', by.y='Group.1')
#Pearson correlations for ES10
cor(ES10.chem.dig$chl, ES10.chem.dig$dig.chl, method="pearson")
## [1] 0.8450695

Plotting Supplementary Figure 2 panel A

ES10.chem.dig.ggplot <- ggscatter(ES10.chem.dig, x = "chl", y = "dig.chl", 
                                                          add = "reg.line", conf.int = TRUE,
                                                          cor.coef = TRUE, 
                                                          cor.method = "pearson",
                                                          xlab = "Chemical chlorophyll 
                                                                  content (ng/sq mm)", 
                                                          ylab = "Digital chlorophyll
                                                          content (ng/sq mm)") + 
                                                          labs(tag = "A")
ES10.chem.dig.ggplot
## `geom_smooth()` using formula 'y ~ x'

Plotting Biomass by Treatment by Condition

(Supplementary Figure 2, Panel B)

# Supplementary figure 2 panel B
## ES5 by dilutions (side by side)
BiomassAndpH.metadata.ggplot.B <- ggplot(BiomassAndpH.metadata, 
                                         aes(x = reorder(Isolate, +Weight_grams), 
                                             y = Weight_grams, fill=FilterWeight)) +
  geom_boxplot() + #geom_point(aes(colour = factor(LeafSampleNumber)))# + geom_jitter()
  scale_fill_grey(start =0.4, end = 1) + labs(tag = "B") +
  xlab("Treatment") + ylab("Whatman No. 1 Filter Weight (grams)") + 
  theme(plot.title = element_text(size = 12, hjust = 0.1, face = "bold"), 
        axis.title.x = element_text(size=10, face = "bold"), 
        axis.title.y = element_text(size = 10, face = "bold"), 
        axis.text.x = element_text(angle = 45, hjust = 1)) + 
  theme(panel.border = element_blank(), 
        panel.grid.major = element_blank(),
        panel.grid.minor = element_blank(), 
        axis.line = element_line(colour = "black")) +
  facet_wrap(~ Condition)
BiomassAndpH.metadata.ggplot.B

Supplementary figure 2 panel C

#ES5 by dilutions (side by side)
BiomassAndpH.metadata.pH.ggplot.C <- ggplot(BiomassAndpH.metadata, 
                                            aes(x = reorder(Isolate, +pH), 
                                                y = pH, fill=pHMeasurement)) +
  geom_boxplot() + 
  scale_fill_grey(start =0.4, end = 1) + labs(tag = "C") +
  xlab("Treatment") + ylab("pH") + 
  theme(plot.title = element_text(size = 12, hjust = 0.1, face = "bold"), 
        axis.title.x = element_text(size=10, face = "bold"), 
        axis.title.y = element_text(size = 10, face = "bold"), 
        axis.text.x = element_text(angle = 45, hjust = 1)) + 
  theme(panel.border = element_blank(), panel.grid.major = element_blank(), 
        panel.grid.minor = element_blank(), 
        axis.line = element_line(colour = "black")) +
  facet_wrap(~ Condition)
BiomassAndpH.metadata.pH.ggplot.C

Supplementary figure 2 panel D

ES8.chem <- read.csv("../raw_data/ES8_chem.chl.csv", header = T)
ES8.digital <- read.csv("../raw_data/ES8.digital.chl.csv", header = T)


ES8.digital.sum <-aggregate(ES8.digital$chl,list(ES8.digital$ExpCode),sum)
names(ES8.digital.sum)[names(ES8.digital.sum) == "x"] <- "dig.chl"

ES8.chem.dig = merge(ES8.chem, ES8.digital.sum, by.x='ExpCode', by.y='Group.1')


FinalpHvsChl.reg <- ggscatter(ES8.chem.dig, x = "dig.chl", y = "FinalpH", 
                                          add = "reg.line", conf.int = TRUE, 
                                          cor.coef = TRUE, cor.method = "pearson",
                                          xlab = "Digital chlorophyll content (ng/sq mm)",
                                          ylab = "Final pH") + labs(tag = "D")
FinalpHvsChl.reg
## `geom_smooth()` using formula 'y ~ x'
## Warning: Removed 11 rows containing non-finite values (stat_smooth).
## Warning: Removed 11 rows containing non-finite values (stat_cor).
## Warning: Removed 11 rows containing missing values (geom_point).

Supplementary Figure 2 composite. Updated on 08/02/2021

gridExtra::grid.arrange(ES10.chem.dig.ggplot, BiomassAndpH.metadata.ggplot.B, BiomassAndpH.metadata.pH.ggplot.C, FinalpHvsChl.reg, ncol=2)
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## Warning: Removed 11 rows containing non-finite values (stat_smooth).
## Warning: Removed 11 rows containing non-finite values (stat_cor).
## Warning: Removed 11 rows containing missing values (geom_point).

Plotting Supplementary Figure 3

Loading dataset (root growth)

ES2.root <- read.csv("../raw_data/ES2.rootMeasurements.csv", header = T)

#Clean dataset for plotting and analyses
ES2.root.noNAs <- na.omit(ES2.root)

Statistical analyses for root lenght

#ES2 longest root statistical analysis
ES2.root.noNAs.lm <- lm (ES2.root.noNAs$Length ~ ES2.root.noNAs$Isolate + ES2.root.noNAs$Condition + ES2.root.noNAs$Concentration, na.action=na.exclude)
ES2.root.noNAs.lm
## 
## Call:
## lm(formula = ES2.root.noNAs$Length ~ ES2.root.noNAs$Isolate + 
##     ES2.root.noNAs$Condition + ES2.root.noNAs$Concentration, 
##     na.action = na.exclude)
## 
## Coefficients:
##                        (Intercept)      ES2.root.noNAs$IsolateDMCC2126  
##                             38.608                             -10.916  
##     ES2.root.noNAs$IsolateDMCC2127      ES2.root.noNAs$IsolateDMCC2165  
##                             -8.786                             -12.099  
##     ES2.root.noNAs$IsolateDMCC2966  ES2.root.noNAs$ConditionStationary  
##                             13.649                              -6.885  
## ES2.root.noNAs$Concentration25fold  
##                            -25.132
summary(ES2.root.noNAs.lm)
## 
## Call:
## lm(formula = ES2.root.noNAs$Length ~ ES2.root.noNAs$Isolate + 
##     ES2.root.noNAs$Condition + ES2.root.noNAs$Concentration, 
##     na.action = na.exclude)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -30.264  -8.173   1.284   7.818  22.674 
## 
## Coefficients:
##                                    Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                          38.608      3.659  10.550 3.33e-15 ***
## ES2.root.noNAs$IsolateDMCC2126      -10.916      5.457  -2.000  0.05008 .  
## ES2.root.noNAs$IsolateDMCC2127       -8.786      5.223  -1.682  0.09781 .  
## ES2.root.noNAs$IsolateDMCC2165      -12.099      4.986  -2.427  0.01832 *  
## ES2.root.noNAs$IsolateDMCC2966       13.649      4.199   3.250  0.00191 ** 
## ES2.root.noNAs$ConditionStationary   -6.885      3.178  -2.167  0.03431 *  
## ES2.root.noNAs$Concentration25fold  -25.132      3.492  -7.197 1.26e-09 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 12.49 on 59 degrees of freedom
## Multiple R-squared:  0.5919, Adjusted R-squared:  0.5504 
## F-statistic: 14.26 on 6 and 59 DF,  p-value: 5.795e-10
anova(ES2.root.noNAs.lm)
## Analysis of Variance Table
## 
## Response: ES2.root.noNAs$Length
##                              Df Sum Sq Mean Sq F value    Pr(>F)    
## ES2.root.noNAs$Isolate        4 4955.4  1238.8  7.9369 3.450e-05 ***
## ES2.root.noNAs$Condition      1  317.6   317.6  2.0349     0.159    
## ES2.root.noNAs$Concentration  1 8084.1  8084.1 51.7926 1.256e-09 ***
## Residuals                    59 9209.1   156.1                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#Tukey's HSD for Variable Condition

ES2.root.noNAs.condition.HSD.test <- HSD.test(ES2.root.noNAs.lm, 
                                              'ES2.root.noNAs$Condition', group = T)
ES2.root.noNAs.condition.HSD.test
## $statistics
##   MSerror Df     Mean       CV
##   156.086 59 26.46406 47.20907
## 
## $parameters
##    test                   name.t ntr StudentizedRange alpha
##   Tukey ES2.root.noNAs$Condition   2         2.829835  0.05
## 
## $means
##            ES2.root.noNAs$Length      std  r   Min    Max    Q25    Q50    Q75
## Shaking                 27.54116 19.14552 37 0.759 67.578 14.983 24.544 36.420
## Stationary              25.08983 18.19797 29 0.982 68.045 13.602 17.404 38.714
## 
## $comparison
## NULL
## 
## $groups
##            ES2.root.noNAs$Length groups
## Shaking                 27.54116      a
## Stationary              25.08983      a
## 
## attr(,"class")
## [1] "group"
#Tukey's HSD for Variable Concentration

ES2.root.noNAs.Concentration.HSD.test <- HSD.test(ES2.root.noNAs.lm, 'ES2.root.noNAs$Concentration', group = T)
ES2.root.noNAs.Concentration.HSD.test
## $statistics
##   MSerror Df     Mean       CV
##   156.086 59 26.46406 47.20907
## 
## $parameters
##    test                       name.t ntr StudentizedRange alpha
##   Tukey ES2.root.noNAs$Concentration   2         2.829835  0.05
## 
## $means
##         ES2.root.noNAs$Length      std  r   Min    Max     Q25    Q50     Q75
## 100fold              33.41979 18.02719 43 2.261 68.045 16.7635 31.069 47.0615
## 25fold               13.45987 11.57407 23 0.759 38.442  1.8595 14.252 19.0160
## 
## $comparison
## NULL
## 
## $groups
##         ES2.root.noNAs$Length groups
## 100fold              33.41979      a
## 25fold               13.45987      b
## 
## attr(,"class")
## [1] "group"
#Tukey's HSD for Variable Isolate

ES2.root.noNAs.isolate.HSD.test <- HSD.test(ES2.root.noNAs.lm, 'ES2.root.noNAs$Isolate', group = T)
ES2.root.noNAs.isolate.HSD.test
## $statistics
##   MSerror Df     Mean       CV
##   156.086 59 26.46406 47.20907
## 
## $parameters
##    test                 name.t ntr StudentizedRange alpha
##   Tukey ES2.root.noNAs$Isolate   5          3.97949  0.05
## 
## $means
##          ES2.root.noNAs$Length      std  r   Min    Max     Q25    Q50      Q75
## Control               25.46106 14.42338 16 0.759 53.277 15.1875 21.304 32.42575
## DMCC2126              23.86656 15.08114  9 2.261 43.013 13.8810 28.594 35.49300
## DMCC2127              13.56456 13.67932  9 1.131 36.420  1.7050 15.283 15.82100
## DMCC2165              18.80955 13.95768 11 0.885 46.821 10.3030 15.075 27.10500
## DMCC2966              37.87933 21.47743 21 0.982 68.045 24.5440 33.212 58.57400
## 
## $comparison
## NULL
## 
## $groups
##          ES2.root.noNAs$Length groups
## DMCC2966              37.87933      a
## Control               25.46106      b
## DMCC2126              23.86656      b
## DMCC2165              18.80955      b
## DMCC2127              13.56456      b
## 
## attr(,"class")
## [1] "group"
#Tukey's HSD for Treatment and concentration
ES2.root.noNAs.leafsec.treat.dil.HSD.test <- HSD.test(ES2.root.noNAs.lm, c('ES2.root.noNAs$Isolate', 'ES2.root.noNAs$Concentration'), group = T )
ES2.root.noNAs.leafsec.treat.dil.HSD.test
## $statistics
##   MSerror Df     Mean       CV
##   156.086 59 26.46406 47.20907
## 
## $parameters
##    test                                              name.t ntr
##   Tukey ES2.root.noNAs$Isolate:ES2.root.noNAs$Concentration   9
##   StudentizedRange alpha
##            4.55324  0.05
## 
## $means
##                  ES2.root.noNAs$Length       std  r    Min    Max      Q25
## Control:100fold               34.51244 12.257238  9 19.375 53.277 26.42600
## Control:25fold                13.82357  6.234620  7  0.759 20.628 13.92700
## DMCC2126:100fold              23.86656 15.081139  9  2.261 43.013 13.88100
## DMCC2127:100fold              25.15625 11.174660  4 15.283 36.420 15.68650
## DMCC2127:25fold                4.29120  6.223480  5  1.131 15.405  1.20100
## DMCC2165:100fold              22.60056 12.426130  9  7.425 46.821 14.98300
## DMCC2165:25fold                1.75000  1.223295  2  0.885  2.615  1.31750
## DMCC2966:100fold              50.63417 17.328417 12 15.108 68.045 43.24375
## DMCC2966:25fold               20.87289 13.073765  9  0.982 38.442 13.88400
##                      Q50      Q75
## Control:100fold  30.2620 41.43500
## Control:25fold   14.8050 16.35950
## DMCC2126:100fold 28.5940 35.49300
## DMCC2127:100fold 24.4610 33.93075
## DMCC2127:25fold   1.7050  2.01400
## DMCC2165:100fold 17.7060 30.29700
## DMCC2165:25fold   1.7500  2.18250
## DMCC2966:100fold 55.6675 64.10850
## DMCC2966:25fold  24.5440 29.70700
## 
## $comparison
## NULL
## 
## $groups
##                  ES2.root.noNAs$Length groups
## DMCC2966:100fold              50.63417      a
## Control:100fold               34.51244     ab
## DMCC2127:100fold              25.15625     bc
## DMCC2126:100fold              23.86656     bc
## DMCC2165:100fold              22.60056     bc
## DMCC2966:25fold               20.87289     bc
## Control:25fold                13.82357      c
## DMCC2127:25fold                4.29120      c
## DMCC2165:25fold                1.75000      c
## 
## attr(,"class")
## [1] "group"

Comparison after normalization of data

# Used the same Tukey's normalization methods used above
ES2.root.tuk = transformTukey(ES2.root.noNAs$Length, plotit=FALSE)
## 
##     lambda     W Shapiro.p.value
## 427   0.65 0.964          0.0525
## 
## if (lambda >  0){TRANS = x ^ lambda} 
## if (lambda == 0){TRANS = log(x)} 
## if (lambda <  0){TRANS = -1 * x ^ lambda}
ES2.root.noNAs.mod = cbind(ES2.root.noNAs, ES2.root.tuk)

#ES2 longest root statistical analysis after normalization
ES2.root.noNAs.mod.lm <- lm (ES2.root.noNAs.mod$ES2.root.tuk ~ ES2.root.noNAs.mod$Isolate + 
                               ES2.root.noNAs.mod$Condition +
                               ES2.root.noNAs.mod$Concentration, na.action=na.exclude)
ES2.root.noNAs.mod.lm
## 
## Call:
## lm(formula = ES2.root.noNAs.mod$ES2.root.tuk ~ ES2.root.noNAs.mod$Isolate + 
##     ES2.root.noNAs.mod$Condition + ES2.root.noNAs.mod$Concentration, 
##     na.action = na.exclude)
## 
## Coefficients:
##                            (Intercept)      ES2.root.noNAs.mod$IsolateDMCC2126  
##                                 10.769                                  -2.553  
##     ES2.root.noNAs.mod$IsolateDMCC2127      ES2.root.noNAs.mod$IsolateDMCC2165  
##                                 -2.390                                  -2.826  
##     ES2.root.noNAs.mod$IsolateDMCC2966  ES2.root.noNAs.mod$ConditionStationary  
##                                  2.501                                  -1.414  
## ES2.root.noNAs.mod$Concentration25fold  
##                                 -5.617
summary(ES2.root.noNAs.mod.lm)
## 
## Call:
## lm(formula = ES2.root.noNAs.mod$ES2.root.tuk ~ ES2.root.noNAs.mod$Isolate + 
##     ES2.root.noNAs.mod$Condition + ES2.root.noNAs.mod$Concentration, 
##     na.action = na.exclude)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -6.015 -1.626  0.381  1.994  4.728 
## 
## Coefficients:
##                                        Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                             10.7694     0.7898  13.635  < 2e-16 ***
## ES2.root.noNAs.mod$IsolateDMCC2126      -2.5526     1.1779  -2.167   0.0343 *  
## ES2.root.noNAs.mod$IsolateDMCC2127      -2.3895     1.1273  -2.120   0.0382 *  
## ES2.root.noNAs.mod$IsolateDMCC2165      -2.8263     1.0762  -2.626   0.0110 *  
## ES2.root.noNAs.mod$IsolateDMCC2966       2.5010     0.9064   2.759   0.0077 ** 
## ES2.root.noNAs.mod$ConditionStationary  -1.4140     0.6859  -2.062   0.0437 *  
## ES2.root.noNAs.mod$Concentration25fold  -5.6168     0.7537  -7.452 4.64e-10 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.697 on 59 degrees of freedom
## Multiple R-squared:  0.5978, Adjusted R-squared:  0.5569 
## F-statistic: 14.61 on 6 and 59 DF,  p-value: 3.856e-10
anova(ES2.root.noNAs.mod.lm)
## Analysis of Variance Table
## 
## Response: ES2.root.noNAs.mod$ES2.root.tuk
##                                  Df Sum Sq Mean Sq F value    Pr(>F)    
## ES2.root.noNAs.mod$Isolate        4 221.55   55.39  7.6175 5.116e-05 ***
## ES2.root.noNAs.mod$Condition      1  12.18   12.18  1.6751    0.2006    
## ES2.root.noNAs.mod$Concentration  1 403.79  403.79 55.5332 4.637e-10 ***
## Residuals                        59 429.00    7.27                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#Tukey's HSD for Variable Condition

ES2.root.noNAs.mod.condition.HSD.test <- HSD.test(ES2.root.noNAs.mod.lm, 'ES2.root.noNAs.mod$Condition', group = T)
ES2.root.noNAs.mod.condition.HSD.test
## $statistics
##    MSerror Df     Mean       CV
##   7.271182 59 7.841521 34.38763
## 
## $parameters
##    test                       name.t ntr StudentizedRange alpha
##   Tukey ES2.root.noNAs.mod$Condition   2         2.829835  0.05
## 
## $means
##            ES2.root.noNAs.mod$ES2.root.tuk      std  r       Min      Max
## Shaking                           8.046515 4.162235 37 0.8359054 15.46584
## Stationary                        7.579976 3.961030 29 0.9882628 15.53522
##                 Q25      Q50      Q75
## Shaking    5.809506 8.006901 10.34835
## Stationary 5.455591 6.403566 10.76748
## 
## $comparison
## NULL
## 
## $groups
##            ES2.root.noNAs.mod$ES2.root.tuk groups
## Shaking                           8.046515      a
## Stationary                        7.579976      a
## 
## attr(,"class")
## [1] "group"
#Tukey's HSD for Variable Concentration

ES2.root.noNAs.mod.Concentration.HSD.test <- HSD.test(ES2.root.noNAs.mod.lm, 'ES2.root.noNAs.mod$Concentration', group = T)
ES2.root.noNAs.mod.Concentration.HSD.test
## $statistics
##    MSerror Df     Mean       CV
##   7.271182 59 7.841521 34.38763
## 
## $parameters
##    test                           name.t ntr StudentizedRange alpha
##   Tukey ES2.root.noNAs.mod$Concentration   2         2.829835  0.05
## 
## $means
##         ES2.root.noNAs.mod$ES2.root.tuk      std  r       Min      Max      Q25
## 100fold                        9.428687 3.551457 43 1.6993990 15.53522 6.247133
## 25fold                         4.874211 3.204748 23 0.8359054 10.71825 1.495429
##              Q50       Q75
## 100fold 9.332817 12.224510
## 25fold  5.623663  6.777533
## 
## $comparison
## NULL
## 
## $groups
##         ES2.root.noNAs.mod$ES2.root.tuk groups
## 100fold                        9.428687      a
## 25fold                         4.874211      b
## 
## attr(,"class")
## [1] "group"
#Tukey's HSD for Variable Isolate

ES2.root.noNAs.mod.isolate.HSD.test <- HSD.test(ES2.root.noNAs.mod.lm, 'ES2.root.noNAs.mod$Isolate', group = T)
ES2.root.noNAs.mod.isolate.HSD.test
## $statistics
##    MSerror Df     Mean       CV
##   7.271182 59 7.841521 34.38763
## 
## $parameters
##    test                     name.t ntr StudentizedRange alpha
##   Tukey ES2.root.noNAs.mod$Isolate   5          3.97949  0.05
## 
## $means
##          ES2.root.noNAs.mod$ES2.root.tuk      std  r       Min      Max
## Control                         7.870193 3.154754 16 0.8359054 13.25107
## DMCC2126                        7.431287 3.579335  9 1.6993990 11.53028
## DMCC2127                        4.788109 3.676082  9 1.0833049 10.34835
## DMCC2165                        6.279095 3.393188 11 0.9236621 12.18390
## DMCC2966                       10.122508 4.300847 21 0.9882628 15.53522
##               Q25      Q50       Q75
## Control  5.860782 7.302137  9.581929
## DMCC2126 5.528069 8.842574 10.176367
## DMCC2127 1.414558 5.884853  6.018691
## DMCC2165 4.513060 5.832668  8.526954
## DMCC2966 8.006901 9.746345 14.093153
## 
## $comparison
## NULL
## 
## $groups
##          ES2.root.noNAs.mod$ES2.root.tuk groups
## DMCC2966                       10.122508      a
## Control                         7.870193     ab
## DMCC2126                        7.431287     ab
## DMCC2165                        6.279095      b
## DMCC2127                        4.788109      b
## 
## attr(,"class")
## [1] "group"
#Tukey's HSD for Treatment and concentration
ES2.root.noNAs.mod.leafsec.treat.dil.HSD.test <- HSD.test(ES2.root.noNAs.mod.lm, c('ES2.root.noNAs.mod$Isolate', 'ES2.root.noNAs.mod$Concentration'), group = T )
ES2.root.noNAs.mod.leafsec.treat.dil.HSD.test
## $statistics
##    MSerror Df     Mean       CV
##   7.271182 59 7.841521 34.38763
## 
## $parameters
##    test                                                      name.t ntr
##   Tukey ES2.root.noNAs.mod$Isolate:ES2.root.noNAs.mod$Concentration   9
##   StudentizedRange alpha
##            4.55324  0.05
## 
## $means
##                  ES2.root.noNAs.mod$ES2.root.tuk       std  r       Min
## Control:100fold                         9.866162 2.2929937  9 6.8660524
## Control:25fold                          5.303948 2.0522425  7 0.8359054
## DMCC2126:100fold                        7.431287 3.5793348  9 1.6993990
## DMCC2127:100fold                        7.994262 2.3727767  4 5.8848527
## DMCC2127:25fold                         2.223187 2.0740415  5 1.0833049
## DMCC2165:100fold                        7.364275 2.6551730  9 3.6808888
## DMCC2165:25fold                         1.395787 0.6676861  2 0.9236621
## DMCC2966:100fold                       12.625081 3.0906845 12 5.8409641
## DMCC2966:25fold                         6.785744 3.3449518  9 0.9882628
##                        Max       Q25       Q50       Q75
## Control:100fold  13.251067  8.400796  9.174522 11.253530
## Control:25fold    7.151500  5.539627  5.764551  6.148213
## DMCC2126:100fold 11.530279  5.528069  8.842574 10.176367
## DMCC2127:100fold 10.348346  5.985232  7.871925  9.880956
## DMCC2127:25fold   5.915345  1.126427  1.414558  1.576299
## DMCC2165:100fold 12.183903  5.809506  6.475574  9.181418
## DMCC2165:25fold   1.867913  1.159725  1.395787  1.631850
## DMCC2966:100fold 15.535225 11.532042 13.630329 14.944422
## DMCC2966:25fold  10.718250  5.528846  8.006901  9.064800
## 
## $comparison
## NULL
## 
## $groups
##                  ES2.root.noNAs.mod$ES2.root.tuk groups
## DMCC2966:100fold                       12.625081      a
## Control:100fold                         9.866162     ab
## DMCC2127:100fold                        7.994262    abc
## DMCC2126:100fold                        7.431287     bc
## DMCC2165:100fold                        7.364275     bc
## DMCC2966:25fold                         6.785744     bc
## Control:25fold                          5.303948      c
## DMCC2127:25fold                         2.223187      c
## DMCC2165:25fold                         1.395787      c
## 
## attr(,"class")
## [1] "group"

Plotting Supplementary Figure 3

#Plate for Supp Figure 3 FINAL (USE THIS ONE, because no differences between Shaking and stat were observed)
ES2.root.noNAs.mod$Isolate  <- with(ES2.root.noNAs.mod, reorder(Isolate, -Length))
ES2.root.noNAs.mod.ggplot.plate <- ggplot(ES2.root.noNAs.mod, aes(x = Isolate, 
                                                                  y = Length, 
                                                                  fill = Isolate)) + 
  geom_boxplot() +
  #scale_fill_grey(start = 1, end = 0.4) +
  #scale_fill_manual(values = c("Control"="green", "DMCC2966"="green", "DMCC2126"="gold", "DMCC2165"="gold", "DMCC2127"="gold"))+ 
  #ggtitle("Root Length at 14 Days After Exposure") +
  scale_fill_manual(values = c("#000000", "#FFFFFF", "#DADADA", "#ACACAC","#666666")) +
  xlab("Treatment") + ylab("Root Length (mm)") + 
  theme(plot.title = element_text(size = 14, hjust = 0.5, face = "bold"), 
        axis.title.x = element_text(size=10, face = "bold"), 
        axis.title.y = element_text(size = 10, face = "bold"), 
        axis.text.x = element_text(angle = 30, hjust = 1)) +
  theme(panel.border = element_blank(), 
        panel.grid.major = element_blank(), 
        panel.grid.minor = element_blank(), 
        axis.line = element_line(colour = "black")) +
  facet_wrap(~ Concentration)
ES2.root.noNAs.mod.ggplot.plate

#dev.off()